Complexity, Digital Media And Post Truth Politics: A Theory Of Interactive Systems [1st Edition] 3030445364, 9783030445362, 9783030445379

Table of contents :
Contents......Page 5
List of Figures......Page 6
Chapter 1: Why Does Nobody Know Anything Anymore?......Page 7
A Crisis of Trust......Page 15
Is It New, Though, and If It Is New, Why Is It Happening Now?......Page 17
MAGA-Worlds and Hyperreality......Page 19
Is Technology a Good or Bad for Being Human in Context B?......Page 23
References......Page 31
Chapter 2: The Complexity Problem......Page 34
An Introduction to Complexity......Page 35
The Structure of This Book......Page 39
Post Meaningful Media......Page 48
The Meaning of Words......Page 50
References......Page 54
Chapter 3: A Systems Theory of Social Reality......Page 56
Assemblages......Page 68
Actor Networks......Page 71
References......Page 79
Chapter 4: How Do Systems Work? Differentiation and Communication......Page 81
Difference......Page 86
Differentiation and Self-reference......Page 88
Functional Differentiation......Page 93
Communication......Page 95
Same but Different......Page 99
References......Page 106
Chapter 5: Finding Perspective......Page 109
Interaction Filtration......Page 111
Perspective......Page 116
Time......Page 119
Relative Differentiation in the Interaction Field......Page 125
A Temporal, Perspectival, Empirical Methodology......Page 130
References......Page 135
Chapter 6: Autobots Assemble......Page 136
A Working Theory of Technology......Page 137
The Search for a Logical Definition......Page 140
Logical Precedents......Page 146
Technology In Vivo......Page 149
Confusion and Logical Webs......Page 152
Affordances and Affects......Page 156
From Social Media Logics to Logical Media Systems......Page 160
References......Page 164
Chapter 7: The Political Public......Page 168
If Politics Were Simple: The Deliberative Utopia......Page 171
Messy, Complicated Politics......Page 174
Not Deliberative, Not Gentle......Page 177
Extreme Mediatisation......Page 180
Logical Inequivalence......Page 187
References......Page 192
Chapter 8: Hypertext Reality......Page 196
Where Do Systems Interact?......Page 198
Hypertext......Page 201
Hypertextual Logic......Page 205
Hypertext and Capital......Page 209
The Political Hashtag......Page 211
References......Page 219
Chapter 9: Principles of an Interactionist Methodology......Page 221
A Question of Empirical Principles......Page 223
Questions of Perspective......Page 226
An API Is a Multiplicity of Different Perspectives, Not All of Which Will Be Realised......Page 227
Making Sense from Multiple Perspectives......Page 231
The Temporal Denominator......Page 234
Temporal Sequencing and Assemblage......Page 241
Agency and Assemblage......Page 244
References......Page 246
Index......Page 249

Citation preview

Complexity, Digital Media and Post Truth Politics A Theory of Interactive Systems Philip Pond

Complexity, Digital Media and Post Truth Politics

Philip Pond

Complexity, Digital Media and Post Truth Politics A Theory of Interactive Systems

Philip Pond University of Melbourne Parkville, VIC, Australia

ISBN 978-3-030-44536-2    ISBN 978-3-030-44537-9 (eBook) https://doi.org/10.1007/978-3-030-44537-9 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: gettyimages / jamielawton Cover design: eStudioCalamar This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Why Does Nobody Know Anything Anymore?  1 2 The Complexity Problem 29 3 A Systems Theory of Social Reality 51 4 How Do Systems Work? Differentiation and Communication 77 5 Finding Perspective105 6 Autobots Assemble133 7 The Political Public165 8 Hypertext Reality193 9 Principles of an Interactionist Methodology219 Index247

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List of Figures

Fig. 3.1 Fig. 3.2 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 8.1

Fig. 9.1 Fig. 9.2

A simple one-degree social network 71 A more complex social network featuring varying degrees of connection72 Slide illustrating possible configurations between two simple systems122 An assumption of normal distribution of possible system configurations around event S1,2a123 An illustration that different observer perspectives may influence the observation of an interaction event 124 The graph demonstrates the enormous influence that a tweet from an account with many followers can have on a measure of persistence, an adjusted visibility metric that is meant to capture the subtle dynamics of Twitter temporality 213 The temporal sequencing of tweets, including discourserelevant tweets for both Pauline and Boris 238 The combined impact of Boris’ and Pauline’s tweets on polarisation within the discourse 240

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CHAPTER 1

Why Does Nobody Know Anything Anymore?

The Lincoln Memorial is a remarkably open space at the heart of Washington, DC. It’s possible to stand at the foot of the steps and look west across the reflecting pool towards the Washington Monument and beyond towards Capitol Hill and, on a clear day, imagine that the world is all blue sky and white marble. People move through the space slowly and deliberately, in much the same way that tourists and visitors move around stately monuments everywhere. The atmosphere is similar in Westminster, at the Reichstag or on the hill at Parliament House in Canberra. We visit mausoleums with the same reservation, deliberately and quietly, spending a little longer on each moment. It’s almost as though democracy and death give us similar pause, as well they might. There is a rhythm to these places, a gentle pulsing of the crowd against the slow grinding of the state. They open at nine and they close at five,1 and those hours are managed in orderly queues, snaking between pleated ropes and chrome bollards, signposted and directed. When I visit, I become a little unlike myself: more serious, affecting something I think is gravitas. I suppose I try to be in the place in a way that I think the place demands—to behave appropriately, as it were, naturally normative. I think I learned at school the game of becoming more serious through acting more serious. Nevertheless, even acknowledging the educational and social conditioning through which we learn to submit to power, I do think that these serious places have a way of impressing their seriousness upon us. They bear down on us through the weight of their stone, the depth of © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_1

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their echo, the wash of their light. No doubt this is exactly what the architects intended—these are buildings that remind citizens of institutional immovability. An architect may have intentions for a building, but it is a complex process to make those intentions material. All sorts of influences shape what happens during construction and then, once brick and mortar have settled, the building itself undergoes transformation and interpretation. When people visit things become exponentially more complex. How we feel and how we act once we spend time in a place can depend on so many things: the place itself, the wider environment, its history, its current use, how it is managed, when we visit and why, where we came from and with whom we make the trip. These things are so obvious they hardly need saying. When people come to a place, they bring complexity with them—and 25 million people visit the memorial grounds each year according to the National Park Service.2 That’s 25 million people staring up at the 16th president, wandering around the plaza in front of the pool, gazing across at the Washington Monument and feeling something about the place and their experience of it. Given those numbers and the human variety that they must represent, it is remarkable that most of us behave so similarly. Not that everyone always behaves the same, of course. Partly through design, partly through history and partly through intentional practice, the Lincoln Memorial is a profoundly political space, and political spaces are also performative. People bring their different politics to them. I suppose that my own behaviour is political, in its way—I assume a posture that signifies a type of deliberate seriousness that I must believe is politically appropriate. That, in turn, must reveal something about my learned respect for the Enlightenment or some ‘grand narrative’ of western liberalism. It’s hard not to be impressed by the memorial; it is a place to be political and to be impressed by politics and the potential of political power. These are the steps where Martin Luther King stood and spoke of his dream in 1963; this is where a huge crowd gathered to oppose to the Vietnam War in 1967, where citizens have headed year on year to be heard or, at least, to attempt to be heard. Hollywood has lionised the space and it stands at the centre of an American political imaginary. It is one of those places where people congregate, where speeches are made and where flags wave, a central plaza in the public sphere. The plaza in front of the memorial is therefore, frequently, a confluence of sorts, towards which different marches and processions aim themselves, as though to arrive is, in itself, a statement of political intent. It is a

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political place in a political city. Marches happen here all the time. One of them, the March for Life, takes place annually in January, when a coalition of religious and socially conservative groups meets to reassert their opposition to abortion rights. They first marched in 1974, a year after the supreme court voted seven to two to decide Roe v. Wade, and in the intervening years the march has become a rallying moment for a particular type of demonstrative conservatism. In 2017 Mike Pence addressed the crowd and in 2018 Donald Trump spoke via a satellite link from the White House—respectively the first vice president and president to do so, which in itself says something about the tenor and the signalling of the event. As a general rule, the March for Life hardly registers in the wider political consciousness. It runs on Fox News, of course, but political attention is normally elsewhere in late January, anticipating the State of the Union address and the start of the legislative calendar. In 2019 the march took place on a freezing cold Friday before a long weekend, almost exactly two years after Trump’s chaotic presidency began with crass warnings of American carnage. It’s been a remarkable and wearying two years, during which every event, large and small, has come to feel somehow both politically significant and immediately forgettable. In mid-January, most of the media was preoccupied with Trump’s government shutdown, which was well on its way to breaking records. If the story of the 2019 March for Life is unfamiliar to you, Wikipedia features a comprehensive account of the events that happened on 18 January. On Friday evening, a short video was uploaded to Instagram and quickly began circulating on social media channels, particularly on Twitter, where an account called @2020fight featuring a profile picture of a Brazilian blogger and with 40,000 followers, first shared it at 11:13  pm. You’ve almost certainly seen the video, even if you’re not a Twitter user, because it was soon streaming on TV news stations and websites globally and continued to do so for days. On those websites, the video is usually paused on a particular scene, in which a teenage boy wearing a red ‘make America great again’ (MAGA) cap stands opposite a man with long greying hair and glasses, who is striking a hand-held skin drum with a stick that looks a little like a conductor’s baton. In the video, the man is singing—or, perhaps, chanting is a better verb—and what’s notable about the boy is that he is not moving. He stands quite still facing the man, less than a metre between them, and he stares at him smirking. When I first saw the video, I thought of the way that Anglo rugby players stare down the Mãori haka before a test match with New Zealand. It

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can be a hard attitude to describe. I say that the boy is smirking, but that is just my judgement and it may be unfair; perhaps it would be better to say that he is smiling or keeps his expression fixed. He glances down briefly and there is a flicker of his expression changing, but when he looks back to the chanting man his face assumes the same mask, with the steady stare and the lips curled slightly upwards. I would say that the look is expressive in a way that feels familiar. It’s an expression I suspect I’ve made myself. It speaks to me of defiance and nerves at the edge of confrontation, an assumed superiority but an uncertainty of how to express it. It is a white and male attitude, and it requires money—or, more specifically, it’s an attitude to confrontation that comes from being unfamiliar with confrontation because of money. The smirk is a defence against the unfamiliarity, the sense that if the world performs for our amusement, then we are safe. The joke is our security. Or maybe it’s the hat? The hat grabs the attention. Is there a more divisive piece of clothing in America in 2019 than the cheap red MAGA baseball cap? No doubt libraries will eventually be written unpicking the cultural, racial and economic prejudices embroidered into that particular piece of headwear. I have to admit that when I see it worn by a grinning, white teenager, I assume all sort of attitudes that may be desperately unfair. Then again, if I had chosen to wear that hat, I think I would know that it is demonstrably political and antagonistic, and I would want to insert myself into that cultural polemic. The hat is the archetypal Trumpian symbol: gaudy, ‘American’ in some entirely arbitrary and self-serving sense, mass produced somewhere far from America, ill-fitting and—so we assume—worn less to please the wearer but more to offend another. The video is remarkable for many reasons, not least because it seems to capture so many of the antagonisms of the American moment. As one journalist responded on Twitter: ‘This era is just a series of extremely heavy-handed metaphors’ (Serwer, 2019). There is the boy in the MAGA hat and there is the man with greying hair, whose clothing and instrument suggest that he might be Native American, an impression that his singing appears to confirm. The video is filmed from over the man’s left shoulder, and so we can see that behind the MAGA boy there are many more boys wearing the same hat or a version of it in white, and Trump emblazoned scarves. They are massed on the steps of the memorial, rising above and around the angle of the camera, so that the viewer feels surrounded by them, like the singing man is, in a way that feels threatening. Some of the boys are laughing and some are chanting, and some are looking like they

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don’t know quite what they want to be doing. They are crowd, both discontinuous and uniform, and in many different ways unappealing. Certainly, that was overwhelming view when the video was first released and picked up by the media. CNN described the boys ‘harassing and mocking’ the man in its Saturday morning coverage, NBC News called them ‘taunting’ teens and Variety reported that the internet ‘erupted in outrage Saturday after a video of young men wearing MAGA hats and attempting to intimidate a Native American man at the Indigenous Peoples March in Washington D.C. went viral’ (Nyren, 2019). Of course, it’s always ridiculous to describe the internet doing anything, as though it becomes homogenised and anthropomorphised by the action, but the distaste and the condemnation was remarkably widespread. For many people, no doubt, the video confirmed an already entrenched view that Trump’s brand of aggressive conservatism was both repugnant and, increasingly, a threat on America’s streets. It is also obviously problematic to claim proof of an internet ‘eruption’, as though evidence of an internet pile-on were somehow a distinguishing mark of a critical or important social moment. It is incredibly difficult to measure rage or revulsion and far too easy to perpetuate a polemic when the intent is to interrogate it. The initial short video reached two million views in a couple of hours on YouTube and currently has nearly five million views, but then Gangnam Style by K-popstar Psy currently has three and half billion views, or near enough. Scale is a difficult construct online. It’s may be more productive to reflect on the mass media (broadcast, print and online) response and the political fallout from the event. It soon transpired that the boys were one of many school groups bussed into Washington to participate in the March for Life. The school was quickly identified: Covington Catholic High School is a private, all-boys school in Kentucky. The school issued an apology and the administering diocese was quick to condemn the behaviour and promise an investigation, but condemnation of the school generally and the smirking boy, in particular, was already overwhelming. The school shut for the week. Both the boy and man were named by the press. The man, Nathan Phillips, an Omaha elder, was attending the Indigenous Peoples March. He was already a prominent figure in the long-running battle to prevent the government from running an oil pipeline through indigenous land at Standing Rock in North Dakota. Commentators contrasted his history (and his activism) against the assumed privilege and apparent racism of the boys. Very quickly, the protagonists in the short video were co-opted by

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America’s warring political cultures, conscripted into a struggle that was far wider—and only tangentially related—to their own political reasons for being together on the steps of the Lincoln Memorial. It’s difficult to guess exactly what it was in the video that so offended liberals because there are so many problematic moments. Perhaps it was something specific, such as the look on the teenage boy’s face or the general tableau, which surely spoke to both modern political tensions and historical grievances in troubling ways. The Democratic congresswoman Debra Haaland condemned the ‘blatant hate, disrespect, and intolerance’ on display and situated the events within America’s turbulent history of discrimination towards native people (Gstalter, 2019). On Twitter at the time I was struck by several things simultaneously. First, there seemed genuine and widespread anger; second, the video was emblematic for many people of the political moment; third, it felt like a strange thing for the broadcast media to be so quite so excited about, especially considering the many headline political stories that week; fourth, it all felt quite reflexive, as though this video was simply the latest, short-lived episode in the Trump show and that we, the audience, were reacting to a script. Soon, as with all Trump-adjacent stories, there was a counter narrative. The students contested the initial version of events and the boy at the centre of the video hired a conservative public relations firm to defend him in the media (Schneider, 2019). A longer video emerged that provided more context to the confrontation, and the possibility of a ‘mis-telling’ by the media was soon being advertised by the president and his MAGA surrogates. The students had ‘become symbols of Fake News and how evil it can be’, Trump raged (@DonaldTrump, 2019). Several publications updated their stories to acknowledge that the scene may have been more complex or nuanced than they had originally reported. Conservative commentators furiously accused the media of bias and the ‘liberals’ of hypocrisy. The Catholic diocese apologised to the students for having been ‘bullied’ into their initial condemnation (Romero, 2019). What had changed? What was the additional context that so undermined the initial reading of the video? The second video was uploaded on Sunday, 20 January. It is an hour and a half long and is still available on YouTube. For most of it, the plaza is not particularly busy. People mill around, a man circles on a Segway skateboard, tourists drift in and out of shot and, for the duration, a man is shouting. It transpires that he is a member of the Black Hebrew Israelites, a minority religious group that combines elements from both Christianity

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and Judaism and believes its members are descended from ancient Israelites. The man is preaching angrily and the video is being filmed by one of his companions, who appears to be quite upset with the skateboarder. The Covington schoolboys don’t appear until the hour and eight-minute mark. When they do, they are already standing on the steps, being neither especially loud nor confrontational. The preacher shouts at them as he has shouted at everyone throughout the video. The ‘preaching’ is definitely hostile at times, and insulting, but until the boys arrive the crowd on the plaza has ignored it, and it seems as though that it exactly what the preacher expects to happen. It’s strange to watch how the confrontation starts, because of course while the second video provides more context it is simply another interpretative angle. The boys appear suddenly poised for confrontation, but that may simply be because of their positioning on the step and the location of the camera, which makes them seem massed in opposition. Certainly tensions escalate quickly. The school group responds to the preachers with chants; one student weirdly stoops to roll a plastic water bottle across the concourse. Is he aiming for the preachers or for the skateboarder? Another boy runs out into the contested space, picks up the bottle and scuttles back. It feels like the boys are performing for each other, behaving in that way that teenage boys do when they try to embolden themselves. They begin whopping and cheering, like a crowd at a prize fight. Another boy runs down the steps and pulls off his school jumper. He throws it to the ground and then his t-shirt too. He leads the entire group in a chest-thumping pep-rally routine, which concludes with the whole group facing the preachers, hunching their shoulders, swinging their arms and, well, roaring I suppose. It’s bizarre how many people with camera phones there are, circling the group, darting between them and the preachers, documenting this inane spectacle for posterity. The cameraman turns his phone on himself. ‘Hey, do you all understand who the real cave man is now?’, he asks. I understand his complaint. The boys’ behaviour is offensive, animalistic; they transform the space through their numbers and their aping aggression. ‘We’re surrounded’, he says, and pans the camera to show that the school group does indeed spill down the steps and across the plaza in an encircling arc. Or maybe the tail of the group is now made of bystanders, observing the confrontation or recording it for themselves. ‘There are five of us’, he says, and directs the camera back on to the boys, who are now bouncing in unison and doing a dance, of sorts. They chop their arms; a boy jumps forward and crouches,

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and the entire group kneels—it looks like choreography and then they jump together. Nathan Phillips appears in the right of shot, leading his small group from the Indigenous Peoples March into the space between the preachers and the schoolboys. They are playing their instruments and singing. Phillips told the Detroit Free Press that his aim was to diffuse the situation but I cannot guess motivations from the video. He definitely places himself between the boys and the preachers, as if to separate the two sides. In the same interview he says that the boys ‘were in the process of attacking these four black individuals’ (Warikoo, 2019). They weren’t but I can understand the perception of threat and see how an attack might be anticipated. The cameraman speaks again: ‘[H]e came to the rescue. Our elder, right there. Look at him.’ The boys dance around him, laughing, still performing for each other. I think Americans might say that they are behaving like a pack of ‘frat boys’, still jumping and chanting. People crowd in behind Phillips, but I can’t tell from the video if they are schoolboys or onlookers. The camera is pushed back and it’s not possible to see the smirking boy standing in front of Phillips. Some members of the group continue to argue with the preachers and then a few turn away—a man shouts ‘hey guys, back it up’ but there is no immediate movement. Soon they are back in a semi-circle around the preachers and Nathan Phillips and his group appear to have gone. The preachers insult them and they, occasionally, retort. Until, suddenly, someone shouts ‘let’s go’, and they do quickly. They are all leaving, heading away towards their buses. The camera turns away from them towards the pool and the Washington Monument, which is bright white in the cold twilight. Another group has formed, holding hands in the circle. ‘What the hell is going on here?’ demands the Black Hebrew Israelite preacher. ‘Now this is a peace circle? Kumbaya?’ I haven’t described everything in the video, of course, but I have tried to identify the footage that informs the conflict between Phillips and the boy, and those initial questions around the uncertainty and what it meant for those involved. I’ve also editorialised, and my interpretation of events is doubtless influenced by my feelings and by my politics. I have said that the smiling boy was smirking, because his expression is what I understand that word to mean. He has said in interviews and in the statement from his PR company that he was not smirking but rather smiling to try and show Nathan Phillips that he meant no ill will. That seems improbable to me given the context and given the way that he stands facing Phillips, but how

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do I know? It may well be true. Obviously, our only access to the subject’s motivation is the subject’s account, which we must then decide if we trust. So how can we know what happened and why it happened that way? The short answer is that we can’t—we must accept that we will only ever see what we think we see and that this may ultimately be what we want to see. In practice, however, our attempts at knowing often seem actively disinterested in seeing at all. What is perhaps notable in the different accounts of the Covington incident, and in the media and social media reaction to these videos, is quite how bad we are collectively at seeing objectively. Now, I am not claiming that the controversy at the Lincoln Memorial was an event so socially important, so epochal, that it should have demanded a collectivised, consensual interpretation of meaning. Of course that’s not the case. There was really no urgent demand that we reached collective certainty quickly or efficiently. It was the sort of event that we are primed to argue over, to politicise, as a way of making new meaning. However, what it also illustrates, I think, is the role that media companies and media technologies play in shaping our ability to see. The Covington controversy was a typical of new type of media event, one that has become increasingly familiar and one that exposes utterly the fallibility of mediated seeing.

A Crisis of Trust It would be exhausting to try and summarise the media response to Covington comprehensively. It may not be sensible either—social media enables such an enormous volume and variety of response, it is usually possible to find a quote to support any position. I have already alluded to the shape of the conversation, at least in outline. At first, many media channels condemned the boys’ behaviour, finding it worryingly representative of many of the more worrying aspects of Trump’s re-emergent ethno-nationalism. As the activist Rebecca Nagel wrote in defence of this interpretation: The truth is, Native Americans experience racial hostility daily, but people rarely pay attention. In the now ancient reactions of the first 24 hours of this controversy’s life cycle, Native voices were the first to report and share the video. Rather than expressing shock, most Native voices expressed a tired familiarity of just how common, how everyday these experiences are. We

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have all seen our prayers, our songs, our culture mocked. We have all been on the receiving end of that shamelessly smug smirk. (Nagle, 2019)

On the other side, the students, their representatives and conservative commentators argued for a different interpretation based on the additional video ‘evidence’. The boys were being harassed by the preachers; they remained remarkably calm under such circumstances; they behaved appropriately; they were being attacked because they wore MAGA hats; the media were persecuting a liberal bias; Nathan Phillips had a criminal record and (or) had misrepresented his military service—the crudity of the wrong versus right struggle was predictable but still somehow remarkable. The material facts of the event itself were quickly dissociated in a struggle over some essential right to claim reality. Disagreement over interpretation escalated into dispute over fact. It all felt somehow emblematic of the new America, a profoundly polarised place, a disintegrating union of two warring states of political being, in which the media—always the media—are the authors and the arbiters of what constitutes reality. It is a weary, stressful and increasingly familiar feeling, a pointless argument which also seems to threaten existential crisis: Our democracy is threatened by growing divisions and growing polarization. In some situations, finding common ground is a nice sentiment and even a noble cause. However, an even greater threat to our Democratic society is losing our collective grasp on reality. Democracy cannot function in a world where the truth is irrelevant, or even worse, where it is manufactured. (Nagle, 2019)

This is not a uniquely American worry. Truth appears to be under threat everywhere. There is a now a commission on Truth, Trust and Technology in the UK, launched by the London School of Economics. Elections across the European continent now come with health warnings about Russian-led disinformation campaigns. Professional trust consultancies warn of a ‘global implosion’ and a ‘crisis around the world’, with public trust in governments, media and business falling almost everywhere (Edelman, 2017). In September 2019, the British prime minister was judged by the supreme court to have lied to the monarch, a ruling that appears inconsequential because the country apparently expects him to lie. Globally, discourse reframes trust as tribal and exclusionary, an issue of division (Lewis, Pond, Cameron, & Lewis, 2019). A ‘new’ populism is

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apparently taking hold—there is a rash of opportunistic political men prepared to fuel confrontation and conspiracy theory—and atop it all, of course, there is Donald Trump, liar in chief. When the most powerful political voice in the western world pumps thousands of untruths into the public sphere every year, it’s easy to feel like an existential crisis for truth and for democracy must be upon us. Surely, though, Trump is not the sole cause of this malaise—most likely he’s not the cause at all. The Vote Leave campaign lied consistently during the 2016 Brexit campaign at a time when the notion of a president Trump still seemed like a poor joke to most of us. The close relationship between the Republican Party, conspiracy and manufactured outrage has been well documented for years (Perlstein, 2012). In Australia, nationalist politicians invented a boat ‘invasion’ to justify a militarised campaign against refugees and then imprisoned them on remote Pacific islands. Fossil fuel companies and their lobbyists contest widely accepted climate science, striving for uncertainty to confuse coordinated action. China is using invented threats to wage an eradication campaign against the Uighurs in Xinjiang (Yeung, 2019). Everywhere we look, in every country, we can find material truths being contested politically, we can observe rational processes being disrupted for profit and we can see the lies piling up. I don’t know if ‘crisis’ is the right word to use but proof of the phenomenon seems inconvertible. Is It New, Though, and If It Is New, Why Is It Happening Now? In terms of newness, and to the extent that novelty matters, there are many different ways in which we can judge disjuncture in the modern moment. In the past couple of decades, globalisation studies has thoroughly debated this question of novelty: the world is clearly changing but is that change continuous or discontinuous? According to Castells (1996), the development of networked computing tools during the latter half of the twentieth century heralded a communication revolution that, in turn, reconfigured all aspects of the human social experience. The changes could only be effectuated because they have at their disposal the global networking capacity provided by digital communication technologies and information systems. … This is, in fact, what separates, in size, speed, and complexity, the current process of globalization from previous forms of globalization in earlier historical periods. (Castells, 2009, pp. 24–25)

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James (2006, p. 22) disagrees that these changes are unprecedented, and instead argues that they are extensions of ‘traditional global connection’; he cautions against any attempt to ‘dehistoricize the process of global extension’. This seems like a reasonable caution to make, especially as it relates to the industrial, financial, ethno-migratory, political and biological inter-connections that now span the globe. These relations are, indeed, largely extensions of historical process that have been connecting human beings and their societies for thousands of years. Populations have moved, merchants have established trading routes and technologies have quickened travel and mechanised labour. Perhaps most familiarly of all, when change has happened, humans have worried about its consequences. Rebecca Solnit (2003) has documented the different ways in which Victorian society was distressed by both the railroad and the camera, one of which seemed to annihilate the established temporal and spatial orders with speed, while the other froze and abstracted it, exposing a weird illusion in movement. Once the North American continent had taken months to cross, and the passage was arduous and perilous. In the decade before the railroad the time had been whittled down to six or seven gruelling weeks, barring accidents. With the completion of the railroad those three thousand miles of desert, mountain, prairie and forest could be comfortably crossed in a week. No space so vast had ever been shrunk so dramatically. (p. 6)

History is a record of change, but our perspective within history limits us so that when the change comes it is always surprising. People, corporations and governments have always lied. Our sense of shared reality has always been constructed. The behaviour of today’s energy companies echoes the tobacco companies’ ugly manoeuvres to delegitimise public health research. Nationalism has a long and varied history of invented imaginaries (B.  Anderson, 1991). Trump and Orban are resurrecting some of the cruder elements of that construction. If our trust crisis is novel, then it is more likely to a novelty of degree rather than type. Except that our trust crisis is enacted through a global media network that never existed before. Could this be one way in which our modern world has stepped away from its history? Is the modern, global mediasphere qualitatively different from what came before it? These aren’t really questions about the types of connection being made: it’s possible to trace the gradual expansion and quickening of media from one historical

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technology to the next, from printing press to newspaper, from telegraph to internet, and thus demonstrated a long process of expansion and inter-­ connection. These questions are more concerned with the effects of this inter-connection. Have we reached a point in our developing techno-­ mediation where it now produces effects that are qualitatively different? Have we crossed a threshold—perhaps one we didn’t even know was there—into a different type of reality? One in which our ‘old’ ideas about truth and consensus are simply no longer applicable.

MAGA-Worlds and Hyperreality This isn’t a book about Trump’s America, fake news or the meme-centric, conspiracy-obsessed media complex that he somehow now heads. However, it is undeniably the case that MAGA-world is a prime (and very present) example of the type of problem that does concern me. In this book I want to explore why digital systems are causing so much trouble for reality, or at least for the practices and processes through which we have previously negotiated what we accept as real. At the same time, I think we need to re-engage with exactly why we find these issues so confusing and so intractable. Look around the world in 2019—there isn’t a coherent or sustained response to the fake-news-conspiracy-madness happening anywhere. Why is this? Partly, of course, it is a consequence of powerful interests being well served by misinformation, but I will argue that we also have a problem with complexity. We haven’t begun to grapple properly with the democratic threat of corporate Facebook because we haven’t yet worked out a way to interrogate the influence of Facebook. It’s hard to frame Facebook as a simple object or as a variable to plug into our empirical models. It is a global, multifaceted behemoth and many of its operations—including its proprietary code—are hidden from oversight. It has many component parts that shape differentially how it exerts influence in the world. I want to establish some principles to ground an interrogation of this complexity and, through those principles, suggest an answer to the ‘why now?’ question. What is it about the peculiar and particular present that is causing so many epistemic problems? One possible answer to the why now question is the one I mentioned earlier: that this is a media-specific problem. Problems like MAGA-world and the wider trust crisis have turned commentators into media critics, and there have certainly been moments in recent years that have had media students knowingly reaching for their copy of Baudrillard. What we are

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witnessing—the destabilisation of ‘truth’, the emergence of an aggressively assertive new symbolic world, full of crazy memes, pictures of frogs and weird conspiracies—are sounds very much like what the French provocateur first described in Simulacra and Simulation. This performative mediaworld is surely what Baudrillard was trying to alert us to when he claimed the Gulf War did not take place? I am far from alone in suggesting that Trump is the first fully hyperreal president. He is called the reality-TV president often enough, but I would argue that the rot goes deeper than this. He is the reality television star who ‘attacks the reality principle itself’ and reveals that politics and democracy ‘themselves might be nothing but simulation’ (Baudrillard, 1981/1994, p.  20). In 1981, Baudrillard looked at Disneyland and at Watergate and diagnosed the problem: America had lost touch with reality; it had invented imitations of itself and preferred to live through those simulations. ‘Disneyland is presented as imaginary in order to make us belief that the rest is real, whereas all of Los Angeles and the rest of America that surrounds it are no longer real, but belong to the hyperreal order and to the order of simulation’ (p. 12). It’s not that Trump is a fake president refusing to adhere to the norms of the role; rather the Trump presidency reveals an emptiness at the heart of the modern democratic process. Politics is performed, judged and enacted wholly through the media. It is cable news content, Twitter-fodder, spectacle for the Sunday shows. The power now lies elsewhere. Hyperreality is Baudrillard’s term for what happens when simulation (imitation) replaces the ‘authentic’ reality that it was meant to be imitating. In MAGA-world, the ‘destruction’ of an opponent on Twitter or the viral success of a meme has largely become the point of political argument. Whereas we once thought of the internet as a distraction from ‘real life’, or as some alternate and separate dimension in which to play politics, we see now that it has become the place to effect politics. Symbolic performance is no longer something that might somehow influence politics, it is politics, and it has become the sphere in which political power is enacted and validated. Of course, this is partly because America has elected a Twitter troll to its highest national office, but it really does matter that Trump values his Twitter performance more highly than other procedural and practical dimensions of his presidency. In very obvious ways, this prioritisation causes real and immediate problems for his administration and for a wider political system that was already highly dysfunctional. Baudrillard’s point, though, is that these challenges to the established

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order are more pernicious than they might originally appear. Trump reveals the performative sham of the presidency and through this revelation drives America ever faster into the weird world of simulation. I am not suggesting that power, material wealth and influence are no longer real in America, but rather that the public sphere—the place in which political meaning is meant to be made and where political action should be shaped—has become fully mediatised. It is a ‘world in which there is more and more information, and less and less meaning’ (Baudrillard, 1981/1994, p. 79); an ‘imaginary concealing that reality no more exists outside than inside the limits of the artificial perimeter’ (p.  14). What marks the perimeter? What has made this world the way that it is now? In this book, I am going to argue two things simultaneously. First, I subscribe to a decidedly Baudrillean response-view, and will argue that it is the media-technological system, as an enabler of capital, which works to promote logics of simulation. That does not mean that technology is the sole cause here, or even mostly to blame, but there seems little doubt that it is deeply implicated in these problems. Much like Baudrillard, I believe that advertising is ‘ground-zero’ for the demolition of meaning (in service to capital); huge political questions are decided by the revenue models through which the digital advertising industry reasserts itself. Second, and hopefully without contradiction, I will argue that complexity is the inevitable product of this digitised-informationalism, and it is complexity that destroys meaning. I am aware that this may present like a familiar and weary ‘information-­ overload’ argument, pitting a deluge of digitised data against the limited processing capacity of the poor human brain—and it is, to an extent. I will argue that one of the main reasons we struggle to make sense of overwhelming complexity is that our epistemological tools are ill-equipped for the environments in which we are trying to deploy them. Where I depart from a more focussed media-centric critique of the problem, is that I do not view complexity as purely a mediatised product but as a more fundamental force of becoming in the universe. In my view, there are obvious reasons why we may want to confront complexity now in our politics, and those reasons are associated with the media technologies that construct the ‘artificial perimeter’ around bizarre events like the Covington scandal. In short, these technologies are remarkably complex—complex in ways that we have not experienced previously—and they are programmed to become exponentially more complex largely through automated processes of internal differentiation. In part, it is the rate of change that is so

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confronting. However, complexity is a challenge that transcends particular spheres. The complexity problem that confuses political events further confuses all our attempts to rationalise and analyse modernity. It is not internet-specific; it is not even media-specific. Why do we talk about technology so much then? Why is the political response to fake news (such that is) so targeted at Silicon Valley? Why am I presenting a book focussed on the technological dynamics of digital media? In part, I think that this is an appealing approach because it is reductively simple. It is easier to frame Covington as a digital media event because we do not know how to grapple with the wider, largely unspoken, psychological possibilities here—the liminal antagonisms and the subliminal desires that drive humans into chaotic action and reaction. This is a period of rapid and unnerving change, in which unexpected things are happening, in which long-established powers are being challenged and are fighting to reassert themselves. One oft-noted consequence of our rush to develop and propagate new technologies is that many of us can remember quite clearly the time before those technologies existed. We may not be acutely aware of the transformation, but we sense it happening, perhaps diffusely or dimly, and we may also sense a vague nostalgia for the earlier time. Or, as has often been the case historically, we find ourselves discomforted, maybe even alarmed, by dangers that seem clear and pressing, and we fret for what we have lost and are deeply concerned by the new world we are creating. There are plenty of good reasons to be worried, I think, about what the internet has done to us, what it does to us still and (more alarming still?) what it may do in the future. Take an hour to Google a sample of the ill-­ conceived, half-baked chicanery from today’s tech-world salesmen. Maybe an argument for compassionate artificial intelligence (AI) sounds worrying, or unleashing the power of the quantum computer seems like something we might want to question before we do it, or maybe the more mundane obsessions of the digital surveillance industry keep you awake? I’m quite confident that you’ll find something that discomforts you. Importantly, technological advances are quite often both material and symbolic, and so we are prompted to imagine both practical and psychological effects. At the same time, we experience them publicly and personally, and so we are able to worry concurrently about Twitter’s influence on our cultural discourse and Instagram’s influence on our private mental health. So this is my simple answer for why we are so focussed on media technologies—we are familiar with framing our worries in this way. We are good at it.

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Won’t Someone Think of the Children I have just said that I think complexity challenges our attempts to parse, analyse and interpret these rapid technological changes. Complexity is an issue for activists and political commentators, but it is also an issue for the ‘professional’ knowledge producers: academics and researchers in both the humanities and empirical sciences. Before I attempt to construct a response to this complexity, I want to conclude this chapter with a discussion of complexity and empirical internet research as practiced. There are two reasons for focussing on empirical research specifically. First is that it has the most influence beyond academia, on policy and public understanding. Second, complexity presents particular challenges for the ‘scientific method’, and many of these challenges are poorly understood or rarely discussed. Problematising these issues is an important first step towards addressing them. There are 95,000 results on Google Scholar for a search connecting social media and happiness among adolescents.3 Newspapers are frequently decrying the damage that ‘social media use’ is doing to our teenagers. A quick database search produces dozens of headlines like ‘Social media health risk to teens’ (The Daily Telegraph, 12 September) and ‘Social media could be classed as “addiction” under calls to protect children from harm’ (The Scotsman, 17 March). Setting aside for the moment the curious social-psychology of displacement happening here (is it simple denialism or our ageing conservatism that makes us scapegoat teenagers in this way?), there are clearly reasons why we think that we may have a problem here. What are they? This is where we first start to run into problems of complexity, and there is clearly some sense in reducing these complexities into a manageable formula to frame our concerns and our research. The formula seems simple: it is essentially the same now as it was when the Victorians were carving up wilderness with their railroads.

Is Technology a Good or Bad for Being Human in Context B? Context is important because it changes the framing of our concern. In late modernity, we have added to the political, economic and moral dimensions of concern this atomised, individualised notion of happiness or wellbeing. So hundreds upon hundreds of the results from that Google Scholar

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search ask, in one way or another, the same question: does social media make teenagers unhappy? After all, ‘this simple question has grown into a pressing concern for scientists, care-givers, and policymakers’ (Orben, Dienlin, & Przybylski, 2019). It is no coincidence that our concern has developed in this way. Internet technology and modern psychology are more than coincidentally aligned. ARPANET (Advanced Research Projects Agency Network) was first developed in the research warrens of the Pentagon in the early 1960s; at a similar time, the practice of psychology was being transformed from an often intuitive, hermeneutic reflection on inner-life (W. James, 1901) into a quantified, industrial-academic diagnostic tool. There is clear evidence that teenagers now use social media more than they did before. This is a deductive fact: there was no social media when I was a teenager.4 Similarly, there is some evidence that today’s teenagers are less happy than previous generations, though I think that there is uncertainty around what happiness is supposed to mean and how we choose to pursue it (Magen, 1996). Furthermore, we suspect that social media use is worrying for teenagers (Bell, Bishop, & Przybylski, 2015; Orben & Przybylski, 2019). Mobile social media has penetrated far further into teenager existence than earlier media technologies ever did. The iPhone is 12 years old: in a decade, these media have metastasised and colonised teenage experience. Pew Research reports that 45% of teenagers are online almost constantly (M.  Anderson & Jiang, 2018). These numbers feel alarming in ways that we might not quite understand, but they reflect also common observations that phones are everywhere in our lives and often we find them distracting or unwelcome (Hassan, 2019). So there are good grounds for questioning the relationship between social media and the way we live. Are we using social media too much? Are we using it in the wrong ways? Are we allowing it to change things that we will regret losing? Are we sufficiently aware of the technology itself and the impact it is having? We begin to fret and we begin to feel that we need answers to these questions. We demand a response from scientists and from policymakers, who commission research from technologists and psychologists, all of whom need to translate that knowledge demand into grant applications for a recognisable funding market. Ultimately, we begin to coalesce around a simple framing of a complex problem: is social media use bad for the happiness of teenagers? I chose this example because researchers at the University of Oxford have just published some results in which they report that, despite the enormous amount of research linking social media use and happiness,

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there is hardly any evidence for such an effect, and what evidence exists is nuanced and slight (Orben et  al., 2019). In their study, the researchers found that ‘social media effects are nuanced, small at best, reciprocal over time, gender specific, and contingent on analytic methods’ (p.  10226). Despite a widespread assumption that the effect is real and pervasive, there is hardly any evidence at all for its existence, and many findings are caveated by the fact that ‘the unknowns of social media effects still substantially’ outnumber the knowns (p. 10227). In short, despite the time and the work (and the money), we don’t really have a simple answer to our simple question. It is a conclusion worth pausing on and considering. While it is not necessary to spend vast amounts of time on the methodology that the researchers used, nor even on the complexities of their conclusions, it is important I think to stop and ask ourselves, what is known here? The question is a simple one but the phenomenon it is meant to interrogate is most definitely not. Even in the parsing and processing of the problem, we are ignoring vast amounts of complexity. What does this cost us? The authors of this recent study are scientists pursuing quantitative empirical knowledge of the happiness problem—they are seeking a relationship between a measure of social media use and a measure of happiness. This is the sort of knowledge that has currency in western societies: policymakers want measurable knowledge because, in theory at least, the hard work of recognition and interpretation has been done already. In the west, I think it’s fair to say that we prefer decision takers and actors, we care a little less for the knowledge workers, and we are mostly uninterested in the messy uncertainty of complexity. A huge body of ‘evidence’ has been collected according to this paradigmatic preference but the authors are none too impressed with it. Most of it, they argue, falls short of various scientific quality tests. For instance, too many studies are cross-sectional, meaning that they capture a moment in time, a single instance in which a group of people are asked to report their happiness and their social media use. This is a curious way to represent social media use, which likely has some sort of rhythm to it (can you say with certainty that you used your phone for exactly the same amount of time this week as you did last week?). It is an even weirder way to think about happiness, which certainly fluctuates from day to day and is almost impossible (for me at least) to remember from one day to the next. Or, as the authors put it, by observing ‘cross-sectional relations, scientists have few means of parsing longitudinal effects from artifacts

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introduced by common statistical modeling methodologies’ (p. 10226). They recognise that enormous complexity is being ignored if we only look at momentary associations between people and ignore the changes that happen ‘within people’ over time.5 There are other complaints too. Studies overinterpret weak or nuanced findings, selectively report even modest negative correlations and adopt (the delightfully oblique) ‘analytical flexibility’ to sift through the data in search of statistical significance. In short, too many studies have either misinterpreted or mismanaged their reading of the data. By framing the problem in this way, the authors are able to present two logical solutions: better data and better methods to sift through the data. Better data is found in the Understanding Society, UK Household Longitudinal Survey, which collects self-report information from the same sample of UK population on an ongoing basis. In recent years, it has included questions on internet use and various wellbeing and happiness measures. Because the survey tracks the same population over time, it allows the authors to consider the within-person effects that cross-­sectional studies do not. Better methods mean ‘a specification curve analysis framework — a computational method which minimizes the risk that a specific profile of analytical decisions yields false-positive results’ (p.  10226). In simpler terms, this is a statistical modelling technique that seeks patterns of association between different variables (e.g. a 5-point scale measure of hours per day spent on social media websites and a six-question-generated interpretation of different life satisfaction domains). The authors are able to track individual happiness measures from one year to the next and they can do so using complex regression analyses that configure the data associations using multiple combinations based on those different demographic, lifestyle and psychometric inputs. There are clearly benefits to this approach. It allows them to ask more complex questions. They can look at between-person effects and within-person effects. They can query if heightened social media use (i.e. more than average) prompts a decline in life satisfaction, or they can ask if the process actually works in reverse. They can break down their findings by gender and they can check and cross-check their findings against all the different variations that their model produces. So, in summary, the authors have good data—indeed, one of the ‘best-­ quality datasets informing vital research in this area today’ (p. 10227)— and they have a complex and powerful statistical model that allows to clean and parse the data intelligently and look for directional associations

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across thousands of combinations of multiple variables. And they find very little. A few combinations produce statistically significant results suggesting that more social media use might mean more unhappiness, but some findings are problematic and some show minor or unconvincing effects. The leading conclusion of the paper is that ‘[t]here might be small reciprocal within-person effects in females, with increases in life satisfaction predicting slightly lower social media use, and increases in social media use predicting tenuous decreases in life satisfaction’ (p.  10227). There are some caveats to this finding too. The authors point out that self-reported measures may not be utterly reliable when it comes to assessing daily social media use and yearly intervals may not be ideal for capturing variations in psychological wellbeing. And so more work is required, more cooperation between experts, as well as better social media metrics (and more regular happiness surveys presumably). Do we feel, then, that we have an answer to the question connecting social media use and happiness? Are we happy that any causal relationship is weak and nuanced? How does this finding reconcile with our sense of change in the world around us? If our children are happy, why do we worry so much about the time they spend on their devices? In my view, we don’t have an answer. In my view, this study—while clearly better than many of those that proceeded, at least as far as the scientific paradigm is concerned—tells us almost nothing about the relationship between social media use and teenagers’ sense of wellbeing in the world. This is not to suggest that it’s bad work; it is impressively complex work, but I think that it is complex in entirely the wrong way. Consider the concerns that the authors have about the data that they are using. Let’s take them in turn: first, the reliability of self-reported social media use. For the entire chapter, I have been using the phrase ‘social media use’ as though it meant something defined and discreet, an object or a thing that we recognise and that we know completely and with certainty. Perhaps you picked me up on this or perhaps you didn’t. It’s something that we do quite often—use social media as though it were a simple, single thing. Indeed, the entire premise of the question—the entire premise of the study I have been discussing—requires that we treat social media in this way. But should we? What is social media? What do we mean when we use the phrase? We have a common understanding of its meaning and plenty of academic definitions available to us. It is a collective noun, clearly, and the social differentiates this media from other types, like broadcast television perhaps. The

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academic definitions tend to highlight how users communicate to each other directly using these media and how they create content for themselves (boyd & Ellison, 2007). These days they might also point to the monopolised media markets, to the power and wealth hoarded in a handful of companies, to the politicised messaging, to the ad-driven surveillance or to the dense, sometimes confusing, rarely read and inconsistently applied ‘policy’ documents, according to which these media are meant to operate. Back in 2013 two Dutch academics attempted to define social media through the logics by which they operate. A logic can be understood as something like a tendency for a technology to operate in a certain way, or a functional rule. According to van Dijck and Poell (2013) social media share four logics: programmability, popularity, connectivity and datafication—and it is these functional tendencies that differentiate social media from mass media communication. This approach is helpful because it adds to the definition some descriptive properties. Social media are communication tools that connect people, managing and shaping these connections through programmable feedback mechanisms, while collecting massive amounts of data on these interactions that can either be fed back into these programs or sold to advertisers (or worse). The logics concept is one I will return to repeatedly in coming chapters. Another way we could define social media is by naming the companies that we use the phrase to describe: Facebook, Instagram, Twitter, Google, Weibo, WeChat, Bebo and MySpace. Of course, no one would include Bebo or MySpace in the list unless they were recording social media use for the UK Household Longitudinal Survey, where it will always be 2005 apparently. But are all these companies the same? Are the social media logics by which they operate so precise, so strict, so limiting that we are prepared to say that these companies are the same thing. Come to think of it, how well does the logics definition describe your experience of social media? Are those four logics upmost in your definition of Facebook, for instance, or is your experience of that technology defined by different parameters entirely? Maybe how you connect matters more, or with whom you connect, the jokes and the memes that you share, maybe that’s what social media is for you? And surely it’s your understanding of social media that matters because you are the one whose happiness we are also interested in here. I think the logics definition of social media is great for certain people (mostly academics) interested in certain approaches to analysing media,

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and keen to argue that all these different technologies can be subjected to certain questions that these academics are equipped to answer. But I also think that there are many ways in which the definition falls short. It hardly captures the subjective experience of using these tools, it subsumes quite different experiences into a narrow range of possibilities defined by arbitrary parameters and it overlooks enormous complexity in the function of these media and the behaviours of the people who use them. Any definition of social media is likely to fall short in this way because we use the phrase to describe an enormous range of different practices, cultures, behaviours, markets, policies, politics and countless other phenomena— and, frankly, we use the phrase because attempting to describe these myriad complexities is mind-bending and exhausting. It’s equally difficult to ‘measure’ how we use social media although the survey records this. The modern Apple iOS can tell us to the minute exactly how long we have spent tapping away at social media, and I am always chastened and alarmed when I check on that number. As a result, I am now fully aware of quite how much I underestimate my usage, and that is despite consciously trying to attend to what I’m doing with my phone. I am quite sure that if the UK survey had asked me to provide an estimate of my daily use in 2014, any number I returned would effectively have been meaningless. What’s more, we are interested in happiness here—are all social media hours the same? An afternoon spent sharing exciting gossip with friends over WhatsApp is surely different from an afternoon spent scrolling picture-­perfect lives on Instagram. It seems pretty obvious that some social media use can make us happy, and some can make us miserable. Why are we counting these experiences as the same? The statistical answer to this question is that you just need to survey enough people. There will be people using social media because they are happy and people using it because they are sad and they will be distributed among the general population in a certain way. As long as your survey sample is large enough, the experiences you capture will be distributed in the same way as the population and you are unlikely to bias your findings by getting an abnormally large number of sad users, for instance. Moreover, trends are what matter rather than momentary fluctuations. This is an answer that yet again obscures enormous complexity. Might happiness online mean that you underestimate your usage—you’re busy interacting with friends, having fun—while unhappiness nudges you towards overestimation? Those hours of scrolling feel different somehow,

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the blank nullity weighs heavy on your time. Or perhaps the inverse is true. A flood of happy experiences might make you feel like more time has passed than it has, while the bored and miserable user is painfully aware of how slow the clock is moving. The point is not that it is wrong to count hours of use and make statistical adjustments to smooth out uncertainties,6 the point is that there are all sorts of nuances and uncertainties about just how these technologies influence happiness, and those complexities are surely relevant if we are hoping to answer fully the question we have asked. A similar kind of complexity-ignorance afflicts the psychological measures of wellbeing. The authors of the study are concerned about the year-­ long intervals between different rounds of the UK survey. If I asked you how happy you had been this year, how meaningful would you find the question? I’m not sure if I could say that this year has been a happy or an unhappy one, because of course it has been both at different times and the year cannot be scored against the sum of those different experiences. It’s the same problem we face when we try to estimate our hours of social media use. In fact, there are all sorts of complexities that self-reported psychological ‘measurement’ can overlook. The full complexity failure of quantitative psychology is not an argument that we need to enter right now. There are epistemological questions relating to representation and the limits of an empirical classification system for subjective semantics have been described (Agamben, 1993; Lacan, 1977/2002). Where does this leave us? We have some numbers that suggest excessive social media may make teenage girls unhappy, and we have some statistical tests that suggest that it’s not a very strong relationship. But there are details missing from this picture, massive numbers of them, and they seem important if we really want to understand the relationship between social media and happiness. Ultimately this is an epistemological question about how we seek to know complexity. It’s hardly innovative to suggest that we need better collaboration—people have been calling for computer, data and social scientists to work together for the best part of a decade. Attempts have been made; results have been mixed. What’s necessary is a route to knowledge about complexity—a roadmap for collaboration. System interactionism strives to meet that challenge. It is an epistemological endeavour, which means of course that it must first articulate an ontology—it must define the world it is seeking to describe.

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Notes 1. The Lincoln Memorial is actually open 24 hours a day. 2. Information about park visitor numbers is provided by the National Park Service: https://www.nps.gov/nationalmallplan/Documents/Media/ NAMA%20Fact%20Sheet.pdf 3. Search: ‘social media happiness adolescent’ conducted 16 July 2019. While the number of results has little meaning in itself, a brief review of the results reveals the range of concerns that academics have sought to address, and exposes the widespread conviction that something damaging requires exploration. 4. I had the New York mega-sitcoms, MTV and the NME once a week to connect me to the heaving rhythms of teenage culture. 5. The researchers are recalling, of course, the age-old philosophical argument about whether it is better to know human beings in time, as messy as that might be, or to imagine them outside of it, idle and idealised in some abstract, often celestial, other-world. It is a pertinent recollection and we will return to this conundrum later in the book. 6. In this scenario time—or, at least, the participants’ ability to estimate time— is what statisticians call a confounding variable. Time might be related to both the ability to estimate hours of social media use (the input variable) and the participants’ sense of wellbeing (the output variable). If it is, then it can be biasing the regression analysis in ways that are hard to estimate. So, strictly speaking, it might be wrong to count hours and hope that data smoothing can iron out the issues.

References @DonaldTrump. (2019, January 22). Nick Sandmann and the students of Covington have become symbols of Fake News and how evil it can be. They have captivated the attention of the world, and I know they will use it for the good – maybe even to bring people together. It started off unpleasant, but can end in a dream! Retrieved from https://twitter.com/realDonaldTrump/statu s/1087689415814795264?s=20 Agamben, G. (1993). The coming community. Minneapolis, MN: University of Minnesota Press. Anderson, B. (1991). Imagined communities: Reflections on the origin and spread of nationalism. London/New York: Verso. Anderson, M., & Jiang, J. (2018). Teens, Social Media & Technology 2018. Retrieved from www.pewresearch.org: https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/

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Baudrillard, J. (1981/1994). Simulacra and simulation. Ann Arbor, MI: University of Michigan Press. Bell, V., Bishop, D. V., & Przybylski, A. K. (2015). The debate over digital technology and young people. BMJ, 351, h3064. https://doi. org/10.1136/bmj.h3064 boyd, d. m., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x Castells, M. (1996). The rise of the networked society (2nd ed.). Oxford, UK: Blackwell. Castells, M. (2009). Communication power. Oxford, UK: Oxford University Press. Edelman. (2017). 2017 Edelman Trust Barometer. Retrieved from https://edelmandotcom.djeholdings.acsitefactor y.com/research/2017-edelmantrust-barometer Gstalter, M. (2019). Haaland condemns students’ behavior toward native elder at indigenous peoples march. The hill. Retrieved from https://thehill.com/ blogs/blog-briefing-room/news/426160-haaland-condemns-studentsbehavior-toward-native-elder-at Hassan, R. (2019). Uncontained: Digital disconnection and the experience of time. Melbourne, Australia: Grattan Street Press. James, P. (2006). Globalism, nationalism, tribalism. London: Sage. James, W. (1901). The principles of psychology. London: Macmillan. Lacan, J. (1977/2002). Ecrits: The first complete edition in English (B.  Fink, Trans.). New York: W. W. Norton & Company Ltd. Lewis, J., Pond, P., Cameron, R., & Lewis, B. (2019). Social cohesion, twitter and far-right politics in Australia: Diversity in the democratic mediasphere. European Journal of Cultural Studies, 22(5–6), 958–978. Magen, Z. (1996). Commitment beyond self and adolescence: The issue of happiness. Social Indicators Research, 37(3), 235–267. Retrieved from www.jstor. org/stable/27522905 Nagle, R. (2019, January 23). I know what I saw when I watched the Covington video. Retrieved from https://thinkprogress.org/covington-catholicvideo-nathan-phillips-nicholas-sandmann-72512dd30615/ Nyren, E. (2019, January 19). MAGA hat-wearing teens harassing native American elder spark condemnation from Hollywood. Retrieved from https://variety. com/2019/biz/news/maga-hat-wearing-teens-taunting-nativeamerican-elder-spark-condemnation-from-hollywood-1203112838/ Orben, A., Dienlin, T., & Przybylski, A. K. (2019). Social media’s enduring effect on adolescent life satisfaction. Proceedings of the National Academy of Sciences of the United States of America, 116(21), 10226–10228. https://doi. org/10.1073/pnas.1902058116

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Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-­ being and digital technology use. Nature Human Behaviour, 3(2), 173–182. https://doi.org/10.1038/s41562-018-0506-1 Perlstein, R. (2012). The Long Con: Mail-order conservatism. The Baffler, 21. Retrieved from https://thebaffler.com/salvos/the-long-con Romero, D. (2019, January 26). Bishop apologizes to teen who faced off with native American. Retrieved from https://www.nbcnews.com/news/us-news/ bishop-apologizes-teen-who-faced-native-american-n963056 Schneider, G. (2019, January 21). Louisville PR firm played a key role in Covington Catholic controversy. Louisville Courier Journal. Retrieved from https://www. courier-journal.com/story/news/local/2019/01/21/covington-catholicrunswitch-pr-helped-student-in-controversial-video/2638400002/ Serwer, A. (2019, January 20). White kids in MAGA hats jeering a native American Vietnam veteran. This era is just a series of extremely heavy-handed metaphors. Retrieved from https://twitter.com/AdamSerwer/status/ 1086679297975296000?s=20 Solnit, R. (2003). The annihilation of time and space. New England Review, 24(1), 5–19. van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and Communication, 1(1), 2–14. Warikoo, N. (2019, January 20). Native American leader of Michigan: ‘Mob mentality’ in students was ‘scary’. Detroit Free Press. Retrieved from https://www. freep.com/story/news/local/michigan/2019/01/20/native-americanleader-nathan-phillips-recounts-incident-video/2630256002/ Yeung, I. (2019, July 1). They come for us at night: Inside China’s hidden war on Uighurs. Retrieved from https://www.vice.com/en_au/article/8xz3qg/ they-come-for-us-at-night-inside-chinas-hidden-war-on-muslim-uighurs

CHAPTER 2

The Complexity Problem

In this book I argue that we are facing a problem of unprecedented complexity and that this problem has at least two dimensions to it. Perhaps our greatest challenge (as social and cultural theorists, as thinkers more generally and as political actors) is responding to these two dimensions simultaneously. We must grapple with the incredible size, speed and interconnectedness of modern social phenomena. Our political and personal lives have global horizons and these landscapes are densely populated with people, ideas and objects both material and immaterial. At the same time, and as the examples in the opening chapter hopefully demonstrate, it is possible to view these complex landscapes from multiple perspectives, and different perspectives often disagree on what they are seeing. The modern media exposes these disagreements every day. They are particularly obvious in politics in an age of acute antagonism and polarisation. The mass media has fragmented in an effort to serve (and profit from) divergent ideologies, and social media—supposedly the great connector— seems to have exacerbated this separation. Our commentary is full of terms to describe this separation: we imagine ourselves in ‘filter bubbles’ or ‘echo chambers’. These terms—which may not be empirically ideal (Bruns, 2019)—speak to that clear sense of postmodern fragmentation, the destabilisation of the word and, with it, any hope for truth (e.g. Derrida, 1967). As I develop my analysis of mediated complexity, I will argue that this sense of fragmentation and destabilisation is more than a postmodern rhetorical device. It is an essential ontological feature of what we call objective © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_2

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reality and something that physicists have recognised for decades. Clearly, we create different perspective through how we choose to view the world around us, but our political biases and social media groups reflect a far more essential and underlying problem. Perspective is an inescapable feature of an interactive reality and it is deeply implicated in the construction of that reality. The first point is relatively easy to grasp but the second will require some explanation and exploration. At the macro level we are quite used to arguments of this kind. As political philosopher Raymond Geuss (2016) writes, there is no “pure observation” of a society with the members of which we are even minimally interacting. Even asking a question of an informant is a kind of minimal interaction—no matter how neutrally the question is phrased, one cannot be sure it will not, by virtue even of being asked, bring about a change in the informant’s attitudes and thus potentially a change in the society. (16)

My argument is simply that the uncertainty generated in every participant-­observer interaction reflects a dynamic that is present in every interaction between everything. Indeed, so fundamental is this process that it calls into question the ontology of things themselves. In the coming chapters, I am going to propose that we leave behind an object-oriented ontology in favour of a relational and system-oriented perspectivism, of the type that certain popular physicists now propose (e.g. Rovelli, 2018; Smolin, 2019). This transition is undoubtedly challenging to make, and there is certainly a danger that I justify it by invoking concepts from physics that I only half understand. So, for the most part, I will limit references to my amateur interest in the quantum and root this argument in critical media theory, systems theory and (I hope) persuasive observation. My aim is to articulate how complex systems come to present themselves for interaction with other complex systems, which I think is essential knowledge for grounding questions of cause, effect and influence.

An Introduction to Complexity We began with a political problem on the steps of the Lincoln Memorial. A large group of conservative schoolboys confronted a small group of Black Hebrew Israelites and Nathan Phillips, an indigenous elder leading his own small group of activists, found himself between them. The

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meeting between those three groups was captured from multiple angles by countless smartphones and those videos were uploaded to different social media channels: YouTube, Instagram, Twitter and Facebook. One particular video of the meeting between Phillips and the schoolboys appeared to capture an ugly but unsurprising truth about Donald Trump’s America. It spoke of division and aggression, racial aggravation and MAGA-ugliness. For many Americans and for several media outlets, the video proved an essential fault in their country’s culture. It was an interaction that confirmed central truths about the protagonists but also about the groups they were seen to represent. This was only partly a political problem. Other videos, taken from different perspectives, didn’t exactly exonerate the boys or even much change the reading of their behaviour, but they cast some doubt on the initial certainty of the narrative. Were the boys provoked by the Black Hebrew Israelites? Were they facing down Phillips in quite the aggressive way that the close shot suggested? A wider angle suggested that he wandered into an already excited group, could his actions have appeared confrontational? Once these alternative perspectives became possible, once they were published, any sense of agreement or certainty around what had happened was lost almost immediately. In truth, there was probably no agreement to begin with; such is the degree of polarisation within American political discourse. Liberals and conservatives interpreted events on the National Mall entirely differently, according to their own assumptions and established ‘knowledge’ about the other side. Even that critique may be too generous because it assumes that political actors were attempting to interpret events faithfully, even if they were influenced by biases and assumptions. That was almost certainly not the case: modern political discourse is warped by deliberate or wilful misinterpretation, disinformation campaigns and conspiracy theory. No doubt, countless Americans saw video of the events and wondered first how they might use that video to advance their own signification struggle. So, in addition to being a political problem, I argued that the Covington scenario was also an epistemological problem. Having failed to agree upon what happened, it becomes impossible to know what happened—our sense of sharing a universal reality begins to fray. Our knowledge is challenged by complexity. This is an issue in politics particularly because there is often little incentive for groups to reach reasoned and rational decisions about what to believe and what to accept as true and justified (e.g. BäUchtiger & Hangartner, 2010). The issue extends beyond politics,

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however. Even when there is consensus around how knowledge should be constructed, we find ourselves struggling to make sense of chaotic inter-­ connection. The reason that it is so difficult to answer a simple question like ‘does social media make us unhappy?’ is that all the elements in that question are incredibly complex and emergent phenomenon. Moreover, the simple idea that A causes B assumes a simple connection between A and B—a reliable and explainable mechanism for A to exert influence over B. In reality, of course, no such simple, repeatable mechanism exists. A and B are almost always complex, their interactions are complex, and complexity introduces variability and uncertainty. It is important to be absolutely clear what I mean when I refer to ‘complexity’. In Chap. 3 I reference both complexity and chaos theories (studiously avoiding the mathematics) and describe a dialectic of creative destruction-reconstruction that I liken to a quasi-mystical enervating dynamic. In later chapters, I locate this cycle of complex/order and chaotic/disorder within systems and, specifically, within the autopoietic logics of meaning production. Given that approach, there is a risk that complexity itself is only ever partially explained. For instance, if its existence is assumed, what was its origin? Was there ever a time in which the world was not ‘complex’? Is all complexity of the same type? What category of being does complexity describe? Where are the simple systems and how are they different1? In short, if complexity is everywhere, what explanatory power does it have? These are challenging questions, though complexity as I understand it does not necessarily need to have explanatory power—I see it as a problem to be addressed rather than a mechanism to solve anything. Nevertheless, at different times in coming chapters, I will talk about the complexity of technology and media, of social systems and of interactive reality more generally. It is perfectly legitimate to wonder if I mean the same thing in every context, especially because complexity (as noted) is already well theorised in mathematics and probability studies. So what do I mean by ‘complexity’? There is both a broad and a narrow answer to this question. The complexity I describe in modern media technologies refers to the same dynamic as the complexity I describe in all material and non-material systems, but of course it is of a particular type and has particular consequences within its technological locale. In definitional discussions of complexity a distinction is often drawn between the relative simplicity of the initial phenomenon and the complex (and unpredictable) outcome. For instance, ‘the most important lesson of complexity

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theory is the demonstration of the diversity of phenomena that can arise through the interaction of simple components’ (Pippenger, 1978: 164). Following Flood (1987: 177), I view complexity as being ‘related to systems via the number of elements and relationships, and later via non-­ linearity, asymmetry and non-holonomic constraints’. Complexity is related to people as well, but as I will describe, people are also systems and so awarding them particular categories of complexity is helpful only to the extent that it orientates our awareness of the general phenomenon. Human complexity is no different ontologically from the complex dynamics that technological systems or biological systems exhibit (Adami, Ofria, & Collier, 2000). In my interpretation, complexity is theoretically quantifiable because it refers to the number of interactions happening within a system in a given period. The sense of this definition will hopefully become clearer in coming chapters. It means that complexity is directly related to change-­ potential and thus to uncertainty, which is inherent in probabilistic perspectivism. In addition to the absolute number of interactions, the variety matters too, meaning that a more complex system will have many interactions of different types. What distinguishes one type of interaction from another? The answer is the interacting components, which are products of internal differentiation processes, which is an idea that will hopefully become clearer as we proceed. How should we engage with this complexity? In this book, I structure my response in two parts. In the first, which includes Chaps. 3, 4, and 5, I attempt to outline the problem of complexity in broad ontological terms. I argue that complexity is an inevitable and essential feature of interactive systems and I propose an epistemological response to this complexity inspired by systems theory. In the remaining chapters, I begin to apply a version of system theory, which I call system interactionism, to the analysis of digital media and political argument. I want to be clear that this application is exploratory rather than exhaustive—it is meant to outline an approach that may eventually prove productive. Chapters 6, 7, and 8 describe three principle systems through which modern political meaning is produced: digital media technologies, the political-public sphere and hypertext. These accounts are necessarily shallow—the investigation and description of any system is enormously complex work. Each system I describe already has a rich field of scholarship dedicated to it, and my descriptions inevitably ignore parts of that scholarship, which will frustrate some readers. This is an unfortunate but inevitable consequence of the

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limited space I have available to me. However, I have tried to select theories and histories that I think explain the differentiation of systems and their presentation for interaction in the contexts that I describe. This rationale is tested in the final chapter, which works through an example of digital-political interaction and explores the various complexities involved.

The Structure of This Book In Chap. 3, I argue that we are constructing knowledge inappropriately. I suggest that we confront the limitations of our current object-orientated epistemology. The idea of an objective, shared reality is causing us epistemological difficulty because reality is not made from objects, it is made from events. Reality is temporal, emergent and interactive. This is both a phenomenological argument and an applied physical argument. One of the reasons that A- and B-type questions are so hard to answer is that A-today may be different from A-tomorrow. The simple fact that A can change over time means that how A influences B can also change, which in turn complicates any sense in which A does influence B. This is particularly a problem when we are dealing with macro social systems, massively complex assemblages, which can differentiate in multiple different ways in different directions at different times. It would be more appropriate, I think, to build our knowledge of reality around these assemblages (or networks of events), and to make interactions between assemblages the primary focus of our epistemic efforts. In making these arguments, I am inspired by evidence from quantum mechanics and time studies, but my approach has clear precedence in Deleuze’s assemblage theory and actor network theory (ANT). I explain why neither of those approaches is fully sufficient for the problem I am describing, and hence why system interactionism is a necessary development. In Chap. 4, I suggest that embracing complexity, moving away from an unchanging view of essential objects and towards an event-based analysis of complex becoming, will align us better with physics, phenomenology and a long-established strain of social theory focussed on the productive potential of difference. Niklas Luhmann’s social system theory helps to fully temporalise the study of interactive assemblage. Luhmann introduced the idea that systems might reproduce themselves through self-referential logics, part of the universal productive pursuit of meaning through communication. The task of explaining system operation is largely an effort to describe the logical processes through which the system reasserts

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difference—that is, difference between itself and its environment, including other systems. If we want to understand relations between systems, then we need to identify interaction events: moments of communicative contact that produce information. Once we have an event-set described, we must analyse critically how the systems interpret that information differently. Any such analysis demands that we have already established an account of historical system differentiation, a view on what logics are most relevant and influential, which in turn implicates us—and our own meaning-­making logics—in the construction of systemic knowledge. This implication adds another layer of epistemic complexity, of course, because not only is reality no longer static and objective, but it now depends upon the perspective of the observer and her internal knowledge-­ producing operations. There are multiple moving parts, some of which are only ever partially available for observation, and the relative positioning and emergent operations of these parts influence how knowledge is produced. More complex and confusing still, but also crucial, is that perspectival uncertainty can influence the objective status of reality itself. What we observe, and how we observe, clearly shapes what we can know but it also shapes fundamentally what is. In Chap. 5 I attempted to engage with the combined complexities of assemblage and perspective, describing three different domains in which empirics could respond methodologically. The first of these I called framing, by which I mean something broadly similar to the familiar usage in the social sciences. Framing shapes our ability to see, to interpret and, consequently, to know any interactive phenomena. It depends upon the knowledge and the assumptions that we bring to the act of observation, but it also depends upon our location within the interaction field. This is a central realisation of the interactive ontology and the consequence of perspective. Framing focuses our attention on events that are likely to be important, given what we already know about the logical operations of our systems of interest. This is largely a book about framing—an attempt to redirect research attention towards the events that shape meaning-making within the digital-political system. This framing work directs us towards signification events in hypertext; it is a conceptual argument for a particular emphasis in our empirical work. The actual empirical work—the business of collecting interaction data and analysing the significance of that data—is hardly advanced by these framing discussions. However, it helps to describe the historical

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assemblage of the digital and political systems because this provides us with a more nuanced, more sophisticated understand of the elements within our question. We are better prepared to interpret critically the influence of the events that we observe. In Chap. 5, I outline the process for moving from framing work to empirical study. It involves three discreet steps: • First, we apply a logical frame to locate events for observation; • Second, we search for changes in the order of logical assemblage: do certain interactions seem to accelerate, redirect or otherwise change the production of systemic meaning; • Third, we interpret those changes comparatively across the different systems involved in interaction—do they reinforce one particular logic over another? Do they align meaning production across systems or are there divergent effects? The principle aim of this book is to coordinate interactionist theory and to advance it a little in support of an empirical methodology that is better equipped to engage with the confounding complexity of modern media systems. Confounding is an apt word to use in this context because it has both its common usage, in which it speaks of surprise or confusion, and a narrower technical definition, used by statisticians to refer to a particular type of bias. I first became familiar with this usage when I was studying epidemiological modelling. Confounding bias, for the epidemiologist, occurs ‘whenever there are important differences between the groups being compared that are also related to the variable of interest’ (Kirkwood, 1988: 162). In epidemiology, confounding tends to be related to broad ‘objective’ categories. For instance, text books will describe an example in which the incidence of a disease is correlated with being male, say, and two study groups, the first of which has a far higher percentage of males in it. Chances are that the first group will have a higher incidence of the disease as well, but we would be mistaken if we concluded that group membership was a factor in falling sick. The uneven distribution of males between the two groups means that there is another factor influencing the incidence rates that we observe. According to this understanding, confounding is an error of study design—something that can be corrected by careful planning, case-control and statistical adjustment. What happens, though, when the network of potentially confounding relationships becomes so dense and complex that

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it is nearly impossible to unpick? What happens when the variables themselves are ‘objective’ categories that become a little unstable once we interrogate them? Under such circumstances, it can be hard to identify the factors that may be related to both variables of (possible) influence and the systems we are seeking to compare. That is particularly a challenge for media use and psychology studies when—as we saw in Chap. 1—complex communication and behavioural phenomena must be quantified via scales and reductive metrics. The interactionist arguments I present in this book are not meant to solve these challenges but I want to encourage a re-engagement with this complexity, what it means theoretically and how it can be approached methodologically. That means that there are a couple of trends that I am arguing against. The first is the rejection of theory, which academics have been railing against since Silicon Valley-types first suggested that their algorithms no longer needed smart minds to interpret them. Chris Anderson’s (2008) article declaiming the ‘end of theory’ has been cited more than 2000 times according to Google Scholar, which is both astonishing and a slightly dispiriting insight into the rhetorical limitations of academics. Like many others before me, I push back against that idea in these pages. The second trend that concerns me is the division of research into camps that are increasingly alien to each other. This may seem a strange issue to be making a fuss over. Science has always had its hermeneutic and empirical parts; the division of quantitative and qualitative labour is happily accepted; we are mostly familiar and comfortable with the idea that there are domains of knowledge (and that they do not necessarily need unification). However, system interactionism is reasserting an ontological claim, which is that reality is always physical and perspectival (scientific and subjective) at once. As such, its empirical methods are meant to be wholly immersed in critical readings of the phenomena being studied. It is not sufficient to approach deep questions about mediated being armed solely with tools from psychology: too much complexity will be missed, and fundamental dynamics will be overlooked. Chapters 6, 7, and 8 attempt this preparatory work for three systems that are centrally important in modern political problems. This is necessary work if we accept that the situated perspective of the observing system is fundamentally implicated in interactionist reality. The chapters are meant to establish in outline a theoretical understanding to underwrite the empirical investigation of interacting systems. Those systems are digital media, politics itself and hypertext, which I argue is a semiotic space

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somewhat distinct from the technology that enables it. Each of these chapters presents an account of the logical differentiation of these systems. The idea of logics is introduced in Chap. 6 and is borrowed from van Dijck and Poell (2013: 2) who use the term social media logic to differentiate digital media from the ‘norms, strategies, mechanisms and economies’ of mass media. The idea of logical differentiation is to align these norms with the repetitive processes and structures of autopoiesis that Luhmann credits with giving shape, sense and persistence to social systems (e.g. Luhmann, 1986; Schwanitz, 1995). The simple idea is that how a system interacts in its next phase of becoming is shaped—at least to some extent—by how it has interacted in the past, especially if there are patterns that have repeated predictably. In Chap. 6 I try to outline a plausible explanation of digital system differentiation grounded in a broadly constructivist account of technology. I focus on the logics of digital media, using the history of its invention to illustrate how periods of social, political and technological interactions have shaped its construction. The idea is to align a recognisable philosophy of technology with my interpretation of a Luhmann-inspired systems theory, and from this position to make sensible methodological suggestions. Digital media systems are viewed as complex assemblages, in which technological elements interact with social and political actors to shape recurring patterns of meaning-making. These patterns effect outcomes that are explainable, even if they are not wholly predictable. The history that I recount is partial as is my engagement with many rich and varied fields: the philosophy of technology, mediation theory, science and technology studies, platform studies and human-computer interaction, among others. Some readers will note correctly that these approaches offer far more critical insight than my cursory review suggests. Unfortunately, this is an inevitable consequence of trying to tell the history of a complex technology in a single chapter. Political theorists will doubtless experience a similar frustration with Chap. 7, in which I paint politics as a reductive contest between normative deliberation and semiotic struggle. This raises a couple of questions, I think. The first is, why bother to tell the history of a system if you are not going to do it particularly well? The second is, by telling these histories in the way that I do, what am I implying about the ideas that I leave out or mention only in passing? Ideally, I would provide a complete and compelling account of digital invention because this is the best possible preparation for the next phase of

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empirical observation. In Chap. 5, I argue that theoretical histories can help direct our attention in the interaction field. If we accept that our limitations as observers shape the reality we observe, then the sensible response is to try and address those limitations. I fully accept that these logical descriptions need to be developed and that there is considerable historical precedent for how this should be done. Others have already done this, of course, with more space and more skill than I have to offer (e.g. Berry, 2011; Feenberg, 1991, 1999). The account that I present in Chap. 6 is not meant as a repudiation of any other approach. I am not suggesting that more in-depth theoretical analysis is unnecessary (it absolutely is!), but I think that the value of this book is that it attempts to start an inter-­ disciplinary conversation. Its goal is to develop an epistemology for INTER-system synthesis instead of INTRA-system specificity. I believe that we are falling short epistemologically precisely because we are not considering complex system events from the multiple perspectives through which they are realised. The historical differentiation of digital technology is important and necessary context because it tells us about how a digital system is likely to present itself for interaction with a political system. My aim in Chap. 6 is to provide sufficient context to explain a critical reading of these systems. That reading emphasises certain logics, specifically automation and networking, which I argue are particularly influential in shaping the productive potential of digital media systems. Automation logics, which were inscribed in the earliest tools of mechanical computation, continue to drive social media towards certain types of data capture and processing. Networking, the architectural logic underwriting modern communication systems, pushes connectivity and expansion. These logical precedents shape a digital media environment that is primed to receive information—and make meaning from it—in particular ways. The history of networked computing helps us to understand how the structures of logical differentiation were shaped during periods of intense interaction, sometimes through invention, sometimes through adaptation or deployment. In each period, we see that the social and political motivations of the state, private enterprise, engineering and academic communities and, finally, the public at large interact to shape the development of a particular affordances: possibilities for future interaction. Reading these affordances, becoming system-literate, requires ongoing engagement with the ways in which logical differentiation orientates the system towards its next phase of interaction.

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Interactions happen between systems, whether those systems are the focus of our analysis or sub-subsystem components of that focus. The outcome of any interaction between digital media and politics depends, in part, upon media logics and in part upon the political system itself—how it is already operating, its own biases and differentiation logics. In Chap. 7 I attempt an analysis of these logics, trying to orientate modern politics within an interactive systems framework. Once again, I do so recognising that this is a partial effort, limited by works and expertise, which will undoubtedly frustrate some discipline experts. In crude terms, I paint politics as a reductive contest between normative deliberation and semiotic power. Obviously, it is far more complex than this, and I recognise that such an approach is a little like claiming the Gulf War did not take place—rhetorically convenient but analytically difficult. The risk in such an approach is that I construct and contrast positions that very few people actually hold. I portray political history as a decline from some utopian ideal that almost certainly never existed. In my account, which only begins at the turn of this century, the political internet is a land of Habermasian promise, in which information is free and publics are engaged and respectful of each other. In my defence, a lot of early theory about the political internet—and perhaps, even, a lot of people practicing politics online—did look to Habermas and his ideal types of political communication.2 My aim is to provide a reasonable account of the logical differentiation of politics in the age of the internet. For some theorists, all action is political, and so is all history; even if I dated my political chapter from the Enlightenment and was interested only in liberal democracy, the subject is far too vast and too varied to cover satisfactorily. So, I have focussed on a brief period of political differentiation, beginning in the late twentieth century, when cable television and the internet transformed political news coverage. In that period, I argue, that politics has become increasingly mediatised, has moved further way from deliberative ideals and, in doing so, has undermined the institutions that were meant to secure those ideas in practice. Digital media has accelerated a trend that was already well established. Its greatest impact, maybe, has been to expose the illusion of ideal speech, to lay bare practices that were previously better disguised or more carefully choreographed. I settle on an account of politics in which ‘truth’ is shaped through a struggle to signify and is a manifestation of mediatised power (e.g. Lewis, 2005; Love, 1989). This analysis is unlikely to convince all readers, not

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least because it is rooted in a strand of critical theory that was always provocative, and there are political and social scientists who have a very different set of logical expectations. My description of political signification is not meant to deny the validity of these different approaches; I am absolutely not asserting that the politics is only propaganda and power. In the systemic account, logics are varied and complex, and multiple dimensions of differentiation can happen simultaneously. Different observers will recognise different interactive trends, and different interactive domains are quite capable of producing different outcomes. There is no reason why American politics and the American media will interact in exactly the same way as their British, Italian, Turkish or Chinese counterparts. What I am trying to do is identify a set of logical precedents that I argue are particularly susceptible to amplification through interaction with the technological logics I identified in Chap. 6. This is the essence of interactionist system theory—the logical patterns within systems create expectations for the interaction between systems. Digital-politics does not assemble as it does because that is the only way it can be—the systems are not deterministic—but because each system has logical tendencies that make some manifestations more probable than others. In Chap. 8, I argue that those tendencies are only amplified by the symbolic medium through which these two systems interact. Interaction is fundamentally a communicative happening. When systems interact, each produces meaning through value assignment within a wider set of symbolic codes. In the case of digital-politics that symbolic exchange happens mostly through hypertext, which is a system itself, with its own tendency to favour certain differentiation logics over others and to produce meaning in particular ways. I have argued elsewhere, as have many others, that hypertext is a language that promotes dissociation and deferral. Hyperlinking embeds webs of meaning within a document so that text can ‘unfurl’ unpredictably on pathways that are largely hidden from the surface reader. In effect, I argue that hyperlinking destabilises the word, which dramatically complicates the mapping of meanings on to signs. At the same time, I suggest that the modern web—an exploratory rather than a constructive space (Joyce, 2003)—is largely a private-corporate system. Consequently, though signification is hidden from the public, the mapping of meanings on to signs is closely monitored by data collection and advertising companies, who tweak their programmable tools to maximise profit, often to the detriment of the political process.

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In the final chapter (Chap. 9) of the book, I attempt to demonstrate how interaction between the logics of the digital, political and hypertextual systems shapes the production of political meanings. In doing so, I try to respond to perspectival complexity using the interactionist framework I developed in the early chapters. In other words, I try to show how different actors interacting with the digital and political systems simultaneously can be observed from multiple perspectives in time to give a clearer account of their influence. System interactionism makes temporal analysis one of its guiding principles because assemblage and meaning production are the products of events not objects. Events can be located temporally and spatially. Recognising ‘spatial’ complexity prompts us to record events from multiple perspectives but this fracturing only further complicates the relationships between events and between systems. We produce multiple records of event sequences, discreet perspectival timelines, but have no way to compare or to synchronise these timelines with each other. The final chapter, then, is an illustration of the principles I have tried to establish but also a problematisation of the role that time plays in coordinating and complicating events in the interactionist system field. The problem is substantial, I think. Complex digital and political systems challenge notions of objectivity partly because they reveal perspectival uncertainty and partly because they enable (and exaggerate) new interpretative possibilities. For many different reasons—increased complexity, big data collection, low network latencies, user-generated content—digital media systems are unprecedentedly fast communication environments. These are ‘real time’ media, massive numbers of events happen near-simultaneously, so that these systems are constantly in flux, always remaking themselves. They are, in short, incredibly fast and highly unstable meaning-making environments, especially compared to the slow and deliberate rhythms of institutionalised democracy (e.g. Harvey, 1990; Hassan, 2012; Pond, 2016, 2020). This suggests two possible effects. First, digital-politics is found to be increasingly at odds with the norms, processes and consensus of institutional politics; alternate mechanism for power transfer and contestation is created, presumably with disruptive consequences for the established order. Second, digital media produces highly unstable political meanings.

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Post Meaningful Media This second point is worth considering in detail because, after all, the destabilisation of political ‘truth’ is the principle concern raised in Chap. 1. Inevitably, terms like post-truth, interpretation and perspective recall postmodernism (Lyotard, 1984), and given that I have already described American politics as hyperreal, I clearly believe that such critiques have something to offer in the current moment. Unlike the more extreme provocations of that movement, though, I don’t wish to deny the possibility of an objective metanarrative. Complexity and perspectivism challenge the capacity of the (always limited) observer to access that world. Following Mead (1932), reality is the sum of all perspectives and is largely inaccessible for that very reason. My analysis of system complexity (Chaps. 3, 4, and 5) and my description of mediatised politics (Chaps. 6, 7, 8, and 9) suggest to me a few things. First, the ‘objective’ world is more complex than we have been prepared to acknowledge to date. In particular, although our cataloguing of this complexity is well advanced, we have tended to focus both on the objective complexity of systems (broadly, in the sciences) and on the subjective complexity of observers (the humanities). We have considerable work to do if we wish to reconcile these two approaches, and we should do so if we are in any way convinced by system interactionism. Second, I judge that digital media systems are more complex than their mass media counterparts, and that they are logically conditioned to exaggerate largely disruptive disjunctions within political discourse. I spend little time discussing it in these pages, but elsewhere I have connected discourse genealogically to political action, and continue to assert that it is vital for framing material and institutional practice (Pond & Lewis, 2019). As I suggest in the introductory chapter, this destabilisation of discourse through complexity is a significant factor in our increasingly performative politics and the angry rejection of rationalism. We might predict that it is very unlikely that interaction with a complex system will be a simplifying experience. It’s possible to imagine circumstance where this might happen, perhaps, but the odds are surely against it. I hope that in the coming chapters I am able to demonstrate how interactive logics are working to increase complexity in political discourse. Automation, networking, dissociation and deferral: the logics of these technologies are both confounding and expansionary. They enable more political speech and more political signification events, more interactions

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both within and between systems. These increases in volume, and the speed at which they are happening, suggest that discourse will be more changeable, and perhaps less predictable. They explain, at least in part, the overwhelming flow of political news, the tightening media cycles and the endless churn of headlines and opinion. However, they don’t, on their own, point in a particular direction—that is, there’s no obvious reason why rapid change should force politics down a particular path. Complexity on its own should not produce dystopian politics, nor drive polarisation nor favour one ideology over another. In theory, a kind of political white-­ noise is just as probable, a soothing and distracting background hum, as opposed to the polarising clamour that we are currently experiencing. As an explanatory tool, then, we might wonder how effective the combination of event observation and logical interpretation will prove. In my reading of social media logic in Chap. 6 and then in my analysis of interactions within the digital-political system, I tend to emphasise the pernicious influence of the digital advertising industry. Modern social media companies capture and sell audience attention to advertisers—this drives the capture of data, which demands expansion through ever greater connectivity. The algorithms that oversee these feedback loops respond to crude popularity metrics—polarising opinion, conspiracy and grotesque spectacle perform remarkably well in this absurd theatre. There is no time for prudence, rationalism or moral judgement. Despite my claims of methodological innovation, I settle on an analysis that will feel fairly familiar to anyone who has read neo-Marxist cultural criticism. This raises a concern that I would like to address before proceeding with the analysis. How is system interactionism any different in practice from these more established analyses? Is there anything to gain from an involved discussion of assemblage, time and perspectivism, if ultimately it produces the same conclusion that Baudrillard was propounding decades ago? To address these questions, I think that it’s worth returning to a question I raised in the introduction and to walk through a possible response so that I can establish how system interactionism adds value to the process. Towards the end of the introduction, I asked the question: does social media use make teenagers unhappy? This question is important enough in itself, but I highlighted it because it was central to a high-profile psychological study that I wanted to discuss. I was interested in the study mostly because of the ways that the researchers were forced to define their variables, imposing a categorical certainty that I argued was reductive. In a

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world of confounding complexities, complex statistical corrections can help, but not if objects of study are reassembling constantly. What if we to ask the same question from the interactionist perspective? Is there something in the relationship between complexity, postmodern uncertainty and the assault on objective categories (like truth) that might be responsible for making users unhappy? Certainly, it’s possible to imagine a causal sequence. According to my analysis, digital systems are profoundly postmodern, logically structured to reward excesses in signification and to exaggerate different perspectives within audiences. This reproduces a hyperreal dystopia in which rational, deliberative decision-making is made impossible and any sense of shared, objective truth is lost. In such circumstances, it’s hardly surprising that a social media user may begin to feel stressed or unhappy. The systems can be bewildering, polarising and destabilising. So, system interactionism aligns with postmodernism in its analysis of post truth media, but that is hardly surprising. Someone who begins a book with Baudrillard is likely to be sympathetic to a structural critique of media excess. The question, really, is whether system interactionism can augment this analysis through its development of theory and methods to engage empirically with system complexity. I hope that in the following pages I am at least able to demonstrate the potential of the theoretical and methodological work I describe. Clearly, though, this work is still preliminary and there is much to test and much more to develop. With luck, that will be the work of future books.

The Meaning of Words Finally, in this chapter, a note on the terminology I use, especially as it relates to familiar names, objects and categories. In Chap. 3, I criticise the object as the foundation of reality. In Chap. 6 I am dismissive of categorical terms like social media for being too vague or too expansive, for obscuring much about the thing they are meant to describe. In an event-based, perspectival reality, where everything is emergent and connected, names can be problematic. Yet names are clearly necessary here because I am writing a book and I don’t know how else to describe the types of thing I am writing about. In Chap. 6 I argue that social media are better described by their logical self-differentiation processes. Ideally, this approach is extended to all systems, to our entire interactive reality. In my understanding, there are no

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categories of object because the systems supposedly within each category will only ever partly share logical similarities. Furthermore, systems are always assembling, always changing, and while sometimes this might mean convergence,3 they are just as likely diverging from each other. The problematic term ‘social media’ is thus replaced by a detailed description of social media logic, and social media technologies are identified by their shared adherence to programmability, datafication, connectivity and popularity. Social media logic helps to return some meaning to a category that has become obtuse. It is useful to interrogate Facebook and Twitter as social media if it focuses our attention on shared logics that are well described and theorised. However, there are a couple of problems with this approach. First, these logical properties are also always in flux, so the discussion about which logics define a system in any given period is ongoing. Moreover, as a categorisation tool, logical differentiation is unwieldy: are social media meant to share all the logics in equal proportion? If a popular social platform chooses not to collect vast amounts of data on its users, is it still social media? What happens if a company prioritises a single logic over all others? Second, logics are fairly impractical as a descriptive device. It takes a long time to describe the logical properties of a system, which is largely why we have names in the first place. Life would be difficult if we insisted on listing all the logical properties of our pets every time we were asked if preferred dogs or cats. My preference for logical description does not solve the essential problem of categorisation in an interactive system field. It can add some specificity to the terms we are using, but the essential problems of complexity, perspective and time still persist. Meanwhile, we still need to operate in this environment, to communicate our ideas and our analysis in something like a timely fashion. Inevitably, then, I rely on established names for objects, even while I worry about their ontological status. In such circumstances, I can aim for specificity at least. There are certain terms that I will use a lot in this book, and it can be easy to let their usage slip a little, to use them interchangeably when the idea is to tie them to specific characteristic of complex systems. For instance, throughout this book, I will repeatedly use the terms digital media and social media and, in theory, each is meant to mean something different. When I use digital media I am referring to systems that engage automation and networking technologies in media production. When I use social media I am referring to the

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companies and platforms that are popularly associated with the term: Facebook, Instagram, Twitter, YouTube and so on. I don’t think this is ideal, and it can be confusing. It raises questions about how social media relate to digital media. Is one meant to be a sub-­ category of the other? This is made more complicated by the fact that I use social media logic to initiate my discussion of digital media systems. It might be helpful, then, if I spelt out exactly how I intend to use each term, to what it is meant to refer, and to ensure that I follow these specifications throughout the remaining chapters. Digital media systems are defined by the super logics of networking and automation. It is this combination of automated processing and digital information transfer (Shannon, 1949) that characterises the logical structure of the digital system. There are very few automation systems that are not also networked to some degree,4 but these instances would require a different descriptive category. Given that all systems are meant to be communicative, the difference between a digital system and a digital media system is more rhetorical than technological. I would classify the internet as a digital system, being the networked computing architecture that supports the World Wide Web, which is a digital media system. All the digital systems that I am concerned with in this book are digital media systems. In my understanding, a media system is one that contributes meaning to the mediasphere (Hartley, 1996). Social media are defined by social media logics (van Dijck & Poell, 2013), which make them a type of digital media system but further differentiated by the interaction between technological, social and cultural processes. The social dimensions of these digital media systems are shaped predominantly by the logics of popularity and connectivity, which is why my criticism of them tends to emphasise corporatisation and the influence of the digital advertising industry. Like most other writers, I use social media as shorthand for the websites and platforms that dominate the public internet: Facebook, Instagram, YouTube, Twitter and Weibo. Within this category, I would further differentiate peer-to-peer messaging services (WhatsApp, Facebook Messenger) on the basis that they are deliberately less public (which is not the same as being private). However, I fully acknowledge that these categorisations are imperfect and that plenty of platforms operate across these boundaries (e.g. WeChat). I use the term political system to refer specifically to communicative politics and, in particular, to systems that produce discourse and to systems that act in response to discourse. This is a narrow framing—I have

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little to say about action-orientated political models. Even within my communicative framing of the political system, I don’t address the organisational potential of digital media. Elsewhere, I have discussed the role of discourse in communicative action frameworks, such as the connective action model proposed by Bennett and Segerberg (2013). In this book, I am focussed solely on the production of political discourse and assume without comment that political actors respond to the political meanings that they produce, share and consume. As noted previously, this approach may frustrate readers who have a broader or more nuanced understanding of political theory. However, as I have also noted, my aim is to explore the complexity involved in communicative interaction between two complex systems. Other types of interaction are clearly possible.

Notes 1. In general definitional discussions of complexity a distinction is often drawn between the relative simplicity of the initial phenomenon and the complex (and unpredictable) outcome. For instance, ‘the most important lesson of complexity theory is the demonstration of the diversity of phenomena that can arise through the interaction of simple components’ (Pippenger, 1978). 2. Although, in fairness to Habermas, he we less optimistic about the potential of the internet in respect to his normative theory, ‘the rise of millions of fragmented chat rooms across the world tend instead to lead to the fragmentation of large but politically-focused mass audiences into a huge number of isolated issue publics’ (Habermas, 2006: 423). 3. The concept of ‘convergence’ has been fairly well developed in media and journalism studies: ‘[T]he boundaries between mass media communication and all other forms of communication are blurring’ (Castells, 2009: 64). Not only are different media forms (texts, audios, videos, software) deeply interactive, they are increasingly delivered via the same communication channels so that the internet, for instance, is a medium through which printed news, radio shows, movies and interactive games all stream. Similarly, the mobile phone has become a near universal delivery device for all forms of media. 4. It has been interesting to watch American election officials argue that the security of their (often archaic) automated voting machines is guaranteed by the simple fact that these machines do not connect to the internet. Of course, these machines are still networked, electoral roll information is transferred on to them and voting tallies are returned, and even though there may be a physical intermediary, datasets are eventually uploaded or downloaded to a remote server.

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References Adami, C., Ofria, C., & Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Sciences of the United States of America, 97(9), 4463–4468. Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired, 16, http://archive.wired.com/science/discoveries/ magazine/16-07/pb_theory BäUchtiger, A., & Hangartner, D. (2010). When deliberative theory meets empirical political science: Theoretical and methodological challenges in political deliberation. Political Studies, 58(4), 609–629. Bennett, W.  L., & Segerberg, A. (2013). The logic of connective action: Digital media and the personalization of contentious politics. New  York: Cambridge University Press. Berry, D. M. (2011). The philosophy of software: Code and mediation in the digital age. London: Palgrave Macmillan. Bruns, A. (2019). Are filter bubbles real? Cambridge, UK: Polity. Castells, M. (2009). Communication power. Oxford, UK: Oxford University Press. Derrida, J. (1967). Of grammatology. Baltimore, MD: John Hopkins University Press. Feenberg, A. (1991). Critical theory of technology. Oxford, UK: Oxford University Press. Feenberg, A. (1999). Questioning technology. London, UK: Routledge. Flood, R. (1987). Complexity: A definition by construction of a conceptual framework. Systems Research, 4(3), 177–185. Geuss, R. (2016). Reality and its dreams. Cambridge, MA: Harvard University Press. Habermas, J. (2006). Political communication in media society: Does democracy still enjoy an epistemic dimension? The impact of normative theory on empirical research. Communication Theory, 16, 411–426. Hartley, J. (1996). Popular reality: Journalism, modernity, popular culture. London/New York: Arnold. Harvey, D. (1990). The condition of postmodernity: An enquiry into the origins of cultural change. Oxford, UK; Cambridge, MA: Blackwell. Hassan, R. (2012). Not Ready for Democracy: Social Networking and the Power of the People, The Revolts of 2011 in a Temporalized Context. Arab Media & Society, 15, https://www.arabmediasociety.com/not-ready-for-democracysocial-networking-and-the-power-of-the-people-the-revolts-of-2011-in-atemporalizedcontext/ Joyce, M. (2003). Siren shapes: Exploratory and constructive hypertexts. In N.  Wardrip-Fruin & N.  Montfort (Eds.), The new media reader (First ed., pp. 613–624). Cambridge, MA: The MIT Press.

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Kirkwood, B.  R. (1988). Essentials of medical statistics. Oxford, UK: Blackwell Science. Lewis, J. (2005). Language wars: The role of media and culture in global terror and political violence. London: Pluto Press. Love, N.  S. (1989). Foucault & Habermas on discourse & democracy. Polity, 22(2), 269–293. Luhmann, N. (1986). The autopoiesis of social systems. In F. Geyer & J. van der Zouwen (Eds.), Sociocybernetic paradoxes: Observation, control and evolution of self-steering systems (pp. 172–192). London: Sage Publications. Lyotard, J.-F. (1984). The postmodern condition: A report on knowledge. Minneapolis, MN: University of Minnesota Press. Mead, G.  H. (1932). The philosophy of the present. London: The Open Court Company. Pippenger, N. (1978). Complexity theory. Scientific American, 238(6), 114–125B. Pond, P. (2016). Twitter time: A temporal analysis of tweet streams during televised political debate. Television & New Media, 17(2), 142–158. Pond, P. (2020). An event-based model for studying network time empirically in digital media systems. New Media & Society. https://doi. org/10.1177/1461444820911711 Pond, P., & Lewis, J. (2019). Riots and twitter: Connective politics, social media and framing discourses in the digital public sphere. Information, Communication & Society, 22(2), 213–231. Rovelli, C. (2018). The order of time. London: Allen Lane. Schwanitz, D. (1995). Systems theory according to Niklas Luhmann: Its environment and conceptual strategies. Cultural Critique, 30 (The politics of systems and environments), 137–170. Shannon CaW, W. (1949). The mathematical theory of communication. Urbana, IL: University of Illinois Press. Smolin, L. (2019). Einstein’s unfinished revolution: The search for what lies beyond the quantum. London: Penguin Press. van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and Communication, 1(1), 2–14.

CHAPTER 3

A Systems Theory of Social Reality

Modern media systems are global, opaque mega-corporations, connecting millions (sometimes billions) of users; they deploy automated decision-­ making on an industrial scale, responding to self-referential logics that often directly contradict established social contracts. These logics are not registered or recorded anywhere, and they are not audited. They control vast mediascapes (Appadurai, 1991), dense networks of fibre-optic pipes through which gush torrents of words and images, connecting global networks of users and abusers. The sometimes funny, often violent, struggle to signify spins in dizzying cycles, we find ourselves distracted and disorientated, unsure where to focus, uncertain what matters. In the previous chapters, I introduced some different ways in which this complexity can be challenging. I argued that our attempts to recognise and to analyse complex events are falling short. I think that we lack a comprehensive and coherent theory of communication for this emergent techno-textual world. We are lost somewhere between the details and the despair—how do we reconcile the dramatic uncertainty at the core of our mediaworld with the dry technicities of databases and algorithms? In part, we are disorientated by our approaches to the problem; it has been a long time since students of the natural and social sciences spoke similar dialects and, in our locales, we have been operating with quite different assumptions about the world. This has created theoretical and practical problems: we are dealing with issues that are manifestly technological and textual, robotic and human, and we don’t know how to share our toolkits. © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_3

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Indeed, were we to find a language that allowed us to talk to each other, it is unlikely to resolve much at this stage. It is no longer sufficient for engineers and anthropologists to share their perspectives on the problem. This is not a problem that has mechanical and animal elements; rather this is a problem that brings man and machine into being through each other. The complexity of this union grows and evolves and we must ask: can our understanding evolve with it? We need a radical mutation in our theory of mediated being—not because digital reality is utterly unlike the mediaworlds that preceded it but because our conceptual and empirical tools have fallen so far behind. In this chapter I outline an approach to techno-social study that is both mechanical and semiotic (material and symbolic), one that has clear theoretical ancestors, but also one that reframes the study of digital media systems in fundamental ways. I call this theory systemic interactionism, which is an imperfect description for many reasons but does, at least, capture the central components of the theory: namely that reality is a construct between systems and that what matters most about a system is how it interacts with other systems. System interactionism is largely built on borrowed ideas. Its strength, I will suggest, comes both from this willingness to borrow widely but also from the mutually interpretable language it develops to synthesise these loaned concepts. This synthesis remains a work in process. It has flaws and uncertainties that are yet to be tested. However, I think it is worth presenting it in its current form because I am encouraged that it appears both overreachingly ambitious and reassuringly familiar (especially to students of Actor Network Theory [ANT], assemblage theory and certain other ideas, around which a consensus has settled in recent decades). At the same time, I believe that it can demonstrate genuine explanatory power across phenomena that are irrefutably complex and confusing. Later chapters in this book will describe and explore specific cases of different systems interacting with each other. Finally, because system interactionism is really little more than a reframing of familiar ontologies, it does not require entirely new paradigms for research; rather it unites existing epistemologies and methodologies—it greases the wheels of collaboration. Before I introduce some key concepts and explore their origin, it is worth restating briefly the elements of the problem to which we are seeking a response. It is reductive, obviously, and somewhat contrived to say that the problem is simply one of complexity, but there is an essential truth to this observation. As the last chapter demonstrated, though our

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challenges are multifaceted, they all flow in one way or another from a disorientating confusion about objects in our world and the tangled mass of relationships between them. We recognise this complexity—it is not like I am the first person to point to it—but recognising it and responding to it are two quite different things. Both theorisation and communication of theory demand simplicity: as individuals, we may imagine solutions of remarkable complexity, but to remember, document and then describe those solutions for other people, typically we respond to such a challenge with reduction, abstraction and symbolic substitution. In the remaining chapters of this book I attempt to use system interactionism to explore the complexities of digital media and to question how these complexities might influence the beliefs and the behaviours of human beings. Many of the examples I use to illustrate this work will involve political beliefs and behaviours because this is the area I tend to focus on professionally. However, system interactionism isn’t a political theory and it isn’t really an internet-specific theory either. Rather, it is a theory that seeks to address the loss of knowledge that happens necessarily when we turn complex, dynamic phenomena first into objects and then into variables, which we plug into models meant to help us understand the world. As I hope I have demonstrated, we frequently recognise the need to address complexity but we address it at the wrong time. We focus on our modelling, using computer power to run a model through thousands of permutations, building intricate statistical proofs for our arguments, forgetting the fallibility (and, often, the essential vacuity) of the variables we have built our model around (Nobus, 2002). System interactionism is a theory about objects or, more accurately, a theory about the inadequacy of objects for understanding the physical and the social processes that shape the world and our experience of it. Physicists have understood the inadequacy of objects for a hundred years, partly because they keep splitting their elementary particles apart and discovering new complexities within. Some of the language of systemic interactionism is borrowed from quantum physics, which is the branch of science that studies the dynamics of objects when they stop being objects. For physicists, this instability becomes apparent at unimaginably small scales: the empirical solidity of our world dissolves and we find that we require entirely different theories to interpret it. In different ways, philosophers have grappled with the problem of objects for millennia. Aristotle and Plato questioned the essence of objects; our belief in an idealised eternity is largely a product of this struggle to understand the repetitive similarity

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of objects in our world. McCumber (2011) argues that western philosophy has largely been an effort to rationalise the essence of objects. In the twentieth century postmodern thinkers (the existentialists, the phenomenologists and the deconstructionists) finally rejected the idea and proposed instead that objects might change depending on how we experience them. Objects are beautiful because they are simple. The idea that the world is full of things—some like us, some very different—allows us to classify and organise the wonders of existence and to locate our place within the taxonomy. This simplicity is also a weakness. It forces us to label or to categorise phenomena so that we can understand them, and this process can be reductive—it can also be mistaken, political and violent (I will return to the struggle to signify later in the book). The world is complex, messy, changeable and mysterious—we need a better theory for this sort of thing. If system interactionism is partly a rejection of objects, it is mostly—and most obviously—an embrace of systems. To make the case that systems are a better concept for understanding the world and our experience of it, I want to begin at the quantum scale where we can observe our world of things break down into diffuse clouds of events. We don’t have to spend long here but it is useful to consider quantum systems for a couple of reasons: first because it introduces the essential characteristics of a system and explains the logic of always becoming; and second because it establishes the essential continuity between the material and symbolic, which is something that we often struggle to reconcile at our own scale of experience. Quickly, I should flag that, from now on, instead of writing the world and our experience of it I am going to use reality. Of course, there are different notions and theories of reality, but as far as I am concerned the word perfectly captures the continuity of the material and symbolic realms: our reality is our lived experience of the world. When physicists talk about systems they mean a discreet set of physical phenomena that they have chosen to isolate and study. A system is like a window they are using to look through, framing a view of the universe that interests them. As Lee Smolin explains, ‘to apply mathematics to a physical system, we first have to isolate it and, in our thinking, separate it out from the complexity of motions that is the real universe’ (Smolin, 2013, p. 38). Really, though, that’s only half the meaning of the ‘system’ because physicists don’t gaze through windows at random hoping for a glimpse of something interesting. They select windows to look through— they are searching for views that they believe will prove insightful. That

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means that systems are chosen for what they represent: they are a collection of physical particles (matter) subject to forces that physicists expect to reveal something about the universe in general. This is the first important principle to acknowledge about systems: 1. Systems are reference frames artificially isolated for analysis but there is an inherent logic to their selection. We must also acknowledge that there is a scalar tension implicit in how we define systems. We plan to study what is inside the frame on the assumption that what is inside will tell us something about what is outside. ‘The key step is the selection, from the entire universe, of a subsystem to study. The key point is that this is always an approximation to a richer reality’ (Smolin, 2013, p. 39). The boundary of the system is like a perimeter fence or a membrane around a cell; it delineates the variables we are interested in proximally from those that we are interested in more distantly or abstractly. There are pressures at this boundary: it is the line where internal and external forces collide, and where intra-system matter pushes out while extra-system matter presses in. How can we make sure the boundary holds? The next chapter is largely concerned with this question. It picks up on the ‘inherent logic’ of system selection just mentioned and explores what that might mean out there, in the world of systems. For now, one way to maintain the integrity of the system is to begin with a list of the variables that constitute the system and exclude everything else. The system is defined from the ‘ground up’, listing the variables we are interested in and observing how they interact with each other—and only with each other. In order for this solution to work, however, we must ignore the weight of everything outside of the system straining to exert influence over our artificially isolated variables. In physics, just like in the social sciences, there are micro and macro worlds and uncertainty about how they should be reconciled. The second lesson that physics can teach us concerning systems is that they are relational. Systems are dynamic, often in flux, always becoming, and we will only ever be able to observe them partially. The physicist and writer Carlo Rovelli has a delightful description to illustrate this point: When an electron collides with an object—the screen of an old television set with a cathode tube, for example—the cloud of probability with which we conceived of it ‘collapses’ and the electron materializes at a point on the

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screen, producing one of the luminous dots that goes into the making of a TV image. But it is only in relation to the screen that this happens. In relation to another object, the electron and screen are now together in a superposition of configurations, and it is only at the moment of further interaction with a third object that their shared cloud of probability ‘collapses’ and materializes in a particular configuration—and so on. (Rovelli, 2018, pp. 79–80)

This curious quantum phenomenon applies equally to the macro systems of our social world with some important consequences: we can only know these systems in the moment that we interact with them and we can only know them to the extent that we interact with them. In my view, this is a fundamental realisation: systems are phenomenological (Vaccaro, 2018). What does this mean? In simple terms, it means that how we look at systems (the perspective we take on them, and the time we spend observing them) will make a difference to the system that emerges. Systems are dynamic: what we see when we look might change when we look away. Earlier, I reported that researchers were concerned that cross-sectional psychological studies leave researchers ‘few means of parsing longitudinal effects’ (Orben, Dienlin, & Przybylski, 2019, p. 10226). This is the same problem. These studies capture relationships between variables frozen in a moment; they cannot compute how these relationships will inevitably change. We can observe a system just once and capture a frozen moment, or we can track it over time, but really how is this different? The psychologists use data from a survey that it is repeatedly annually, which allows them to track changes from one year to the next, but the system is still being observed through a series of frozen moments. The survey provides a single perspective on a network of relationships connecting social media use and happiness. We hope that the relationships captured in the survey represent what exists at other times, but how can we know? For 364 days a year, the psychologists cannot be sure exactly what configuration the system is taking. For centuries philosophers, scientists and social theorists have struggled with the apparent separation of the subjective and objective worlds. Why do we experience the world in such an intimate and variable way and yet hold to the idea that we experience a similar world together? How can we share a single reality if it is constructed from fluid, private experiences? How can we know that the world will continue the same once we have left it behind? Indeed, how can we know that anything exists when we are not there to experience it?

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Systems thinking allows us to consider these questions in a new way. Another idea from quantum mechanics can help to illustrate how: the superposition of states. The quantum physicist argues that a particle only assumes a concrete position at the point of measurement. Outside this frozen moment, the particle’s position is unknown; it continues to exist but it does so in an uncertain state of fluctuation between different possible positions. This is the lesson we learn from Schrödinger’s poor cat: trapped inside the box, it is both alive and dead until the moment when we check (Schrödinger, 1935). This is how quantum physicists and social philosophers can understand each other. In some sense, when we decide to observe a system we create the system that we will observe. The choices we make will ultimately influence the system we see: where we stand, for how long we look, the instruments we use—ultimately these will influence the configuration we observe. Systems exist in a probability cloud of possible configurations until the moment an observer trains her gaze upon them. Then, for a moment they are fixed, measured and recorded, but the perspective of the observer matters. From a different angle or a different time, the system might look different. The likelihood that it does, ultimately, depends on its pace of becoming. 2. Systems are objectively real but exist in a distribution of possible states until the moment we observe them. Observation is always partial, so the perspective of the observer is paramount. The product of partial sight and perspective creates an impression of subjectivity. Time is central to science and philosophy and we have been struggling with—and arguing about—its influence for thousands of years. Physicists continue to debate the role that time plays in the universe and they are not arguing over minor matters or semantics (e.g. Barbour, 1999; Smolin, 2013). Social theories are similarly preoccupied with temporal dynamics: Marx’s historical materialism, Heidegger’s phenomenology, Foucault’s genealogy—time is in all of them. Large sections of this book are devoted to the study of systems in time. It is curious then that we have, for so long, held on to a worldview predicted on objects outside time—things that do not change. The problem with this view is that it makes the same mistake as the cross-sectional survey: it mistakes what it observes in a moment for what always exists. There is a fundamental difference between a world made from things and a world made from events:

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The difference between things and events is that things persist in time; events have a limited duration … in fact, even the things that are most ‘thing-like’ are nothing more than long events. The hardest stone, in the light of what we have learned … is in reality a complex vibration of quantum fields, a momentary interaction of forces, a process that for a brief moment manages to keep its shape, to hold itself in equilibrium before disintegrating again into dust. (Rovelli, 2018, pp. 87–88)

In fact, I think the quote above has it slightly wrong. The difference isn’t that things (objects) persist in time, because events can have a duration too. Persistence is the idea that the same event keeps happening over and over again—the quantum fields in the stone continue to vibrate in the same way, which is why the stone does not appear to change. We mistake this repetitive happening for stasis—we assume that objects are static while they persist in time because they persist in the same state in which we first observe them. Ultimately, this is not a question of duration, which is always relative according to scale, but of replication versus change. When the stone fractures or erodes it ceases to be a stone—it becomes something different, we make it a different type of thing and we turn it into dust. The problem with this approach is that we miss something fundamental about the dynamic of the stone, which is that long before we swapped labels, it was already becoming dust. 3. A systems-based ontology is different from an object-based ontology because systems are temporal. Systems are interactive, event-based and relational. Systems are always becoming. Quantum mechanics and Heidegger are largely in agreement—according to both, being in the world means being in time, experiencing an event-based existence and occupying this dynamic, mysterious state of always becoming (Heelan, 2011). Reality unfolds around us—we create our world through our being in it, and through those interactions, we ourselves are created. In this sense, system interactionism rejects the subject-­object duality that has characterised so much western philosophy and continues to shape our approach to the empirical sciences. If we think that we can isolate ourselves from the system we are observing, then we are mistaken. This is a clearer articulation of what an event-based ontology necessarily involves. Systems are everywhere and everything. The world is made

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through interactions between systems and between sub-systems within systems. We are systems ourselves. Organs full of cells form life-generating pathways; there is communication, coordination and regulation. With every added layer, the complexity of interaction increases. Somehow, through these networks of interaction, we can become aware of our being—we recognise the unifying logic of ourselves and value it most highly. Also, it is clear that our experience of an external reality is formed through our interaction with other systems in the world. Interactions are the events through which the world becomes itself. Interaction can take many forms, but when two systems interact then both systems are changed and complexity increases. Systems exist in relation to other systems. As the stone moves towards dust, then the dust must move towards stone. A systems-based ontology is derived from quantum ideas but it is subjected to general relativity too. 4. Unless we are dealing with elementary particles, all systems are collections of sub-systems (differentiating together in time according to some unifying logic). The idea that there is a bridge between the empirical sciences and phenomenology is incredibly exciting to me. System interactionism is an attempt to test the strength and the flexibility of this bridge. Consequently, to work with systems, we must engage with ideas that have largely been separated by academic discipline and, of course, by politics. In the not-so-­ distant past, philosophers would experiment, and scientists were metaphysicians. A systems approach demands a re-engagement between the natural and social sciences, so it is another call for collaboration between experts. However, it is also a framework to support that collaboration. The beauty of event-based ontologies is that events can be observed and their effects can be measured. What does it mean to be always becoming? The idea that stones and dust are always moving towards each other is reminiscent of the Heraclitan worldview of constant flux. It is an evocative, poetic idea but not infallible (Russell, 1946). Indeed, it produces some logical problems for the empiricist especially. Stones are clearly stones and clearly not dust, and they remain that way for a very long time. Is there much benefit in blurring highly useful and hardly contentious classification methods? Furthermore, if everything is always changing, relative to everything else, how is our world so clearly full of categories: that is, why are there types of thing that

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are all similar and all stable? Clearly, we live in a world of object-like things. How are we any closer to explaining how our world is the way that it is? Surely that is the point of theory? Rather than focus on change, do we need to concentrate on explaining stasis? Or is it possible to develop the description of systemic change to provide a better explanation of systems themselves? Returning to Heidegger, the paired concepts of becoming and time are central to developing a coherent, powerful account of systemic change. On the surface, the solution appears simple: some systems move slowly and some move fast. The challenge is explaining these divergent speeds. However, dig a little deeper and it is soon apparent that ‘move’ in the earlier sentence is figurative. Some systems move literally, of course, but this is not about trying to explain the relative speeds of the cheetah and the gazelle. The movement we’re seeking to measure is something different entirely; it belongs to the cheetah, certainly, but also to the stone and the human being and to processes, organisations and structures in both the physical, social and symbolic worlds. It is the movement of becoming, which is the movement of systems developing through their internal logics against external pressures; it is movement in time and through time, and the relentless increasing of complexity. The second law of thermodynamics states that entropy within a system only ever increases. Entropy is a measure of thermal energy, but it is also a measure of the molecular disorder or randomness of the system. For physicists, this ever-growing physical disorder is foundational for interpreting natural phenomena. For instance, the arrow of time (its apparent irreversibility) can be explained by this entropic logic—or, at least, by the peculiar way that we perceive this logic when it is manifested at a scale we can observe. In the physical world, then, if we are seeking to explain the foundational ‘movement’ of systems, then entropy might be a good place to start, but this hardly seems applicable to complex social systems. More often than not, social systems are notable for their apparent order and not their disorder. The legal system, for example, is a vast, prescriptive, ordering institution painstakingly constructed to impose rules and punishments on society writ large. Similarly, the human body, as described earlier, is a carefully regulated network of intelligent systems functioning (for the most part) harmoniously. A language of disordering seems manifestly inappropriate. In mathematics, the different logics of chaos and complexity theory provide a helpful analogy:

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Chaos is the generation of complicated, aperiodic, seemingly random behaviour from the iteration of a simple rule. This complicatedness is not complex in the sense of complex systems science, but rather it is chaotic in a very precise mathematical sense. Complexity is the generation of rich, collective dynamical behaviour from simple interactions between large numbers of subunits. (Rickles, Hawe, & Shiell, 2007, p. 934)

Whereas chaos recalls the unpredictable disordering of the entropic universe, complexity appears to describe a quite different process: the wonderfully improbable creation of order from randomness. Are these two dynamics as diametrically opposed as they appear? Not at all, in fact the opposite is true: chaos and complexity are intimately related. Indeed, as researchers have shown, complexity originates in the bountiful variety that chaotic disordering creates (Doebeli & Ispolatov, 2014). In simple terms, chaos delivers a cosmically vast array of materials and contexts, and an infinity of possibilities from which form and structure can emerge. This is yin and yang, the twin enervating dynamics of life, two sides of the spinning coin of complexity. The key realisation here is that nothing is made simpler through these processes. Objective-order is a temporal arrangement. Systems form structures and relationships and systems persist for a while before they disintegrate, but throughout this rhythmical rise and fall, complexity increases. How does this work? If the combination of parts into a more complex system represents increasing complexity, then surely the fracturing of that system back into its components represents a reversal? There are two responses to this problem. First, perhaps it is conceivable that complexity decreases in an isolated system, but systems are not isolated. Systems exist in relation to other systems—in specific relationships, in wider networks, connected by cosmic forces. Changes in one system reverberate elsewhere; chaos theory proves that the apparent reduction of complexity in one system can provoke ‘spooky’ changes elsewhere (McHarris, 2011). Second, are we so sure that a system fracturing actually reduces complexity? Admittedly it may produce new systemic structures that, on their own, appear less complex. A stone is tangibly more complicated than a grain of dust: it has greater scale, engages more atoms in more relationships and holds those relationships through time. When a stone becomes dust, however, a multitude of new systems are created, each perhaps simpler internally, but now released to travel more widely in the systemic network, forming new relationships, building new systems. When a system

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fractures its complexity is not destroyed; rather it is dispersed and the overall levels of disorder and possibility climb a little bit higher. What’s more, we are discussing complexity still using quite narrow terms—essentially counting material units and the connections between them. This is complexity as network science and it is limited. When the system fractures into its components it ceases to exist in only the most literal, material terms. In other ways it persists and those ways can be meaningful and complicated. The stone is no longer the best metaphor here, but we can still imagine ways in which those many emancipated dust particles might recall their shared history: their origin gives them a common starting location in the world so that, in the vast cosmic cloud of possible futures, they may align a little more closely. Meanwhile, their originating stone-ness may persist through patterns in their molecular and atomic structures, vague traces of their unified past. Perhaps the analogy works better if we imagine instead the break-up of our favourite band. There will be no new albums but the ‘band’ can persist in our collective memory, through our replaying of the music, through the weary solo careers still to come and through the irrefutable influence on musicians still learning. Systems rise and fall in time but complexity always increases. 5. Systems produce complexity through the interactions between their component sub-systems. We can study systems by observing the interaction events that increase the productive complexity of the system overall. The image of dust drifting away on the wind is a reminder that we have travelled some way from our starting place, where we were attempting to contend with the complexity of modern social systems, particularly technologies and behaviours. The digression has been important but so is the recall: in this context we are dealing with complexity not chaos; we are seeking to explain stable material and symbolic systems that have influence in the world. That influence, in large part, depends upon that stability, and so we are looking for ways to describe and to explain the stability that we observe. Stability implies some kind of logical order or structure that can persist through time or, more specifically, that can persist despite extra-systemic chaos. Systems have unifying logics: they are ordered complexity. They exhibit collectivised dynamical behaviour. Generally, then, if we are seeking to study systems, we have two types of question available to us. We can ask how this order emerges—that is, we can try to explain the existence of

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systems through an analysis of their unifying logics. We can study the emergence of complex systems from their component sub-systems, an approach that we might call intra-systemic study. Alternatively, we can ask how this collectivised behaviour exerts influence in the world. We can study the relationships that form between systems or observe events where one system conflicts with another. That is, we can focus our attention on extra-systemic study. In other words, we can study the emergence of ordered complexity and we can study the influence of ordered complexity. What else is there? Almost all research is predicated on one of these two types of question. The natural sciences query the evolution and influence of physical and material systems; the social sciences adapt the same interrogative logics for human-centric systems. Indeed, one could argue that the social science discipline only fully emerged when the Victorian imperialists began to apply the logics of evolutionary science to their social observations (Ritzer, 2012). The history of the social sciences is largely a story of the many assumptions and prejudices that have distorted each generation’s analysis of emergent complexity.1 The point, here, is not to rehash the many disagreements and confusions of the discipline, but rather to locate the central concerns of social scientists and natural scientists as being of the same type. Broadly speaking, we are all trying to explain how the world came to be as it is and (being that way) how it now works. We are all students of complexity. That is an exceptionally good thing because it means that the system theorist does not have to begin anew. Reinterpretation should be easier than reinvention. Although we try to develop theories and tools specifically for the study of emergent systemic complexity, we acknowledge that there are plenty already available to us. Personally, I find it both reassuring and exciting to discover synergies between the systemic account of social complexity and more established ideas. As I said at the start of this chapter, system interactionism is largely borrowed and there’s no shame in that.

Assemblages As established, our aim is to analyse how systems exert influence over other systems, and to evaluate possible effects of that influence. We want to be able to question the relationship between social media use and happiness in a way that grasps the incredible complexity of that relationship. To pursue this aim, systemic interactionism argues that we must first

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understand how individual systems emerge: we must observe the interaction events that increase the productive complexity of those systems overall. The focus on interaction events is key here. If we hope to observe how one system interacts with another, we need to understand the internal dynamics of the respective systems; we need to identify the component sub-systems and to describe the logics through which those components assemble into a productive whole. Through assembly ‘a whole may be both analysable into separate parts and at the same time have irreducible properties, properties that emerge through the interaction between parts’ (Delanda, 2006, p. 13). The word ‘assemble’ is used deliberately. Many different words could be used to describe the process through which a selection of sub-systems unite in the production of complexity. We could talk of systems ‘growing’ if we were primarily interested in size and scale; we could describe them ‘evolving’ if we wanted to connote selection and fitness; we might use ‘change’ if we wished to deny any sense of direction or we might use ‘emergence’ if we wanted to give an impression of a more organic visibility. In social study, we choose our metaphors with an eye on how they have been used previously. In this case, assemble is the right choice partly because it denotes the building of something more complex from component parts but largely because it recalls a rich and influential lineage of social thought. In social philosophy, the assembling metaphor is most immediately associated with the work of Gilles Deleuze and Félix Guattari (1980), for whom an assemblage is a multiplicity which is made up of many heterogeneous terms and which establishes liaisons, relations between them, across ages, sexes and reigns— different natures. Thus, the assemblage’s only unity is that of a co-­ functioning: it is a symbiosis, a ‘sympathy’. It is never filiations which are important, but alliances, alloys; these are not successions, lines of descent, but contagions, epidemics, the wind. (Deleuze & Parnet, 1977, p. 69)

In Deleuze’s work, the assemblage metaphor emerges as something elegant and powerful, an engaging concept that appears to capture intuitively so much of what we observe and sense about the complexity of the world around us. It remains, however, an idea that is hard to pin down, perhaps because there are ‘relatively few pages dedicated to assemblage theory … hardly amount to a fully-fledged theory’ (Delanda, 2006, p. 8).

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It is a metaphor that must be explored and developed through a relational reading of Deleuze’s work, which is challenging. As such, it can feel as though assemblage theory is both a comprehensive and compelling account of complexity and at the same time something elusive and obtuse—a evocative and poetic metaphor, perhaps, but lacking explanatory power. Perhaps the most concerted and convincing attempt to develop a fully fledged, coherent and applicable assemblage theory is found in the work of Manuel DeLanda (2006, 2016). For DeLanda, assemblage theory is primarily ontological and its power lies in its realism or its ability to engage with ‘objective processes of assembly’ (DeLanda, 2016, p. 8) and that it can be applied to both natural and social entities: This theory was meant to apply to a wide variety of wholes constructed from heterogeneous parts. Entities ranging from atoms and molecules to biological organisms, species and ecosystems may be usefully treated as assemblages and therefore as entities that are products of historical processes. This implies, of course, that one uses the term ‘historical’ to include cosmological and evolutionary history, not only human history. Assemblage theory may also be applied to social entities, but the very fact that it cuts across the nature-culture divide is evidence of its realist credentials. (p. 8)

Once this nature-culture synergy is identified and its objective realism accepted, it is remarkable how this Deleuzian language mirrors the description of quantum systems discussed earlier. Consider Rovelli’s stone, for instance, which is ‘a momentary interaction of forces, a process that for a brief moment manages to keep its shape’. How similar that sounds to the Deleuzian multiplicity, a temporary alliance of heterogeneous elements in co-functioning liaisons. There is considerable overlap between the principles of systemic interactionism and assemblage theory, especially as DeLanda interprets and extends it. The bifurcated approach discussed earlier, in which intra-­system and extra-system interactions are analysed as distinct but complementary, echoes DeLanda’s relations of interiority and exteriority, which offer separative perspectives on the interactive potentials of systemic parts. According to DeLanda, assemblages are characterised by their ‘relations of exteriority. These relations imply, first of all, that a component part of an assemblage may be detached from it and plugged into a different assemblage in which its interactions are different’ (Delanda, 2006, p.  13). This

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characterisation is primary because it emphasises the rejection of the alternative, which is to define an assemblage as an organic, unified whole defined by relations of interiority. This rejection of unitary ‘wholeness’ is the reason why assemblage theory is so potentially powerful, because it recognises that components of a larger system continue to interact with each other and, potentially, with more distant externalities. Given these apparent similarities, it is worth asking, why is there a need to differentiate system interactionism as separate from assemblage theory, which is innovative, insightful and far better established? Why not accept DeLanda’s formulation wholesale and seek to apply it to the peculiar complexities of modern internet technologies? There are two answers to this question. The first answer is that the systems approach is derived from the language of the physical sciences and that is important, especially given the relevance of quantum insights and the centrality of time to the systems method. The second answer is that there are potential limits to the assemblage method developed by DeLanda (Karaman, 2008) and that these could be addressed by a more holistic, systemic approach. For instance, DeLanda’s assemblages come into being either through a process of territorialisation, through which the assemblage becomes stable, or through deterritorialisation, which leads to destabilisation and potentially new exteriorities. These processes are diametrically opposed although they can occur simultaneously. The result is a fluid mixing and remixing of structures, an ontology that ebbs and flows in way that are evocative but largely elusive. It is, as Jacobs notes from a geographer’s perspective, ‘a realist ontology that purports there are processes that though not directly observable, nonetheless underpin contingent or surface realities’ (Rogers, 2018). In effect, while assemblage theory may be ontologically compelling, it faces real epistemological challenges. This is especially problematic for the sort of enquiry we are seeking to pursue here. Any evaluation of ‘effect’ or impact, of one system on another, demands some sort of empirical framework for analysis, which in turn demands that assemblage processes are identifiable and (ideally) measurable.

Actor Networks At the time of writing it is unclear if an empirical assemblage theory will emerge, but we don’t have to look far to find another rhizomatic theory that does support empirical methodologies. The continuities between

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assemblage theory and Latour’s articulation of actor networks are pretty clear, not least because Latour has repeatedly invoked Deleuze’s rhizome metaphor to describe the essential character of things. There’s not space here to give a full or satisfying account of actor-network theory (ANT), the meaning of which Latour has consistently problematised and reasserted (e.g. Latour, 1999). However, it is important to acknowledge the relevance of ANT to this discussion and to recognise some implications for systems interactionism. Like assemblage theory, ANT is clearly a response to the complexity and unitary instability of objects in the world: Take any object: At first, it looks contained within itself with well-delineated edges and limits; then something happens, a strike, an accident, a catastrophe, and suddenly you discover swarms of entities that seem to have been there all along but were not visible before and that appear in retrospect necessary for its sustenance. (Latour, 2011, p. 797)

ANT is similarly concerned with how ‘the production of object and of objectivity is totally transformed now that they are portrayed simultaneously in the world and inside their networks of production’ (p. 798). It captures the essentially transformative or translational element of being, the ‘movement’ between micro and macro forms, and the deployment of associations: ‘[I]t starts from irreducible, incommensurable, unconnected localities, which then, at a great price, sometimes end into provisionally commensurable connections’ (Latour, 1996, p.  371). Moreover, ANT recognises the essential material and non-material dynamics of things, it makes explicit that objects are ‘at the same time natural, social and discourse’ (p. 369). How do things have these properties simultaneously? It’s possible because things are themselves networks of associations. When NASA launches a rocket into space, the ‘action of flying a technical object has been redistributed throughout a highly composite network where bureaucratic routines are just as important as equations and material resistance’ (Latour, 2011, p. 797). Moreover, ANT originates as a ‘method to learn from the actors without imposing on them an a-priori definition of their world-building capacities’ (Latour, 1999, p. 20). That is, it comes with an empirical toolkit for observation and documentation—there is a way to describe actor networks and it is largely derived from ethno-methodology (Callon & Latour, 1981). Those methods ‘designate a mode of inquiry that learns to list, at

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the occasion of a trial, the unexpected beings necessary for any entity to exist’ (Latour, 2011, p. 799). List is an apt description: ANT methodologies are descriptive; they entail the painstaking collation of entities, the connections between them and the flow of action: ‘Being connected, being interconnected, being heterogeneous is not enough. It all depends on the sort of action that is flowing from one to the other’ (Latour, 2004, p. 64). So while ANT shares with assemblage theory a complex, dynamic, relational and scalar ontology of complexity, it extends its metaphor-driven criticism into methods for observation and analysis. Latour presents this descriptive work in such a way that it seems almost tailored to the complexity challenges posed at the start of this chapter: [T]he things people call ‘objective’ are most of the time a series of clichés. We don’t have a good description of anything: of what a computer, a piece of software, a formal system, a theorem is. We know next to nothing of what this thing you are studying — information — is. How would we be able to distinguish it from subjectivity? In other words, there are two ways to criticize objectivity: one is by going away from the object to the subjective human viewpoint. But the other direction is the one I am talking about: back to the object. Don’t leave objects to be described only by the idiots … a computer described by Alan Turing is quite a bit richer and more interesting than the ones described by Wired Magazine. The name of the game is to get back to empiricism. (Latour, 2004, p. 66)

So why a need for system interactionism? If the problem with assemblage theory was that it did not suggest a method, and ANT does this—it maps the insights of Deleuze on to an empirical-ethnographic toolkit— then why do they not adopt ANT and proceed directly to the descriptive analysis of action in digital media networks? Well, partly because the application of ANT is rarely as straightforward as this brief discussion might suggest. As Latour likes to say, ANT is ‘a negative argument’ (e.g. Latour, 2004, p. 62), a way of looking rather than a way of explaining. As a theory, or even as a methodology, it can seem cryptic. There is a reason that I have relied so heavily on Latour’s own words to describe the ideas and to relate them to the current discussion. Quite often, reading ANT, I am reminded of the assemblage experience: a frustration with evocative but elusive metaphors.

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In part, perhaps, that is merely a product of linguistic choices, especially the use of the word network, which Latour himself is now quick to criticise. Its meaning has changed, he says, with the development of ‘networked’ technologies, from being something transformative or translational into something engineered and affirmative. So not only may ‘network’ be the wrong descriptive term, even if the analogy were appropriate there are questions about how useful it would be. Networks make very poor metaphors, perhaps because we have such a limited visual language for realising them. Or perhaps there is a deeper problem. Wilhelm Baldamus derided the network for having ‘hardly any explanatory power’ (Baldamus, 1982/2010, p. 107), which is a critique taken up recently and enthusiastically by different theorists. The problem is that ‘network sociology is conceptually vacuous as it is merely applying a different range of labels to objects identified in the world, and will often apply a number of different labels to the same phenomena’ (Erickson, 2012, p. 913). That might not be such a problem if we were faithful to Latour’s instruction to observe rather than to attempt explanation. Ultimately, perhaps, it does not matter if we use a metaphor to characterise a thing as long as we observe it as it is, without mediation or distortion. But this is a challenge indeed. Erickson criticises ANT theorists for allowing a ‘slippage between network as metaphor and network as object in the world’ (p. 916), which is a tendency that has surely intensified with the development of the internet and our attendant interest in it. When that happens, when the network becomes object all over again, we have simply replicated our problem rather than solving it. A far bigger issue, in my view, is that the descriptive methods suggested by the network metaphor do not adequately capture the temporality of becoming that is essential to any phenomenological ontology as we have established. ANT is epistemologically—and empirically—quite static. ANT methods trace networks of association; they list entities and connections, define ‘netdoms’ and ‘worknets’ and, at their most extreme, calculate density and centrality ratios, fully imposing an objective network structure on the research subjects. These associations may change, of course, but they only change empirically when they can be redrawn by ANT through additional tracing. Without these repeat observations, the network is a rigid, unchanging essence. In other words, ANT methods recreate the same stochastic observation intervals as periodic surveys. A network—whether metaphorical or actual—remains an object when we inhabit a fundamentally event-based reality.

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The point here is quite subtle and perhaps a little controversial because it contradicts Latour’s claim that ANT is an unfiltered way of seeing. Theoretically, yes, ANT provides a far more dynamic, translational ontology to be seen, but its methods for seeing do not fulfil this promise. In fact, the ANT ‘lens’ imposes some significant limitations on our ability to observe dynamic complexity. One way to think about this problem is to begin with a very familiar and straightforward example of networked association: our friend list on Facebook. Now, admittedly, it’s not really fair to collapse ANT into social media network analysis because ANT is a far more insightful and complex idea, and an ANT theorist would most likely complain bitterly than I am misrepresenting the principles of the theory. However, the point here is not to critique ANT theory itself, but to establish the limits of network methods—and these methods do bear comparison. The temporal limitations of social network analysis do extend to broader, more critical methods of association. In very simple terms, if I were a social network scientist, I might argue that I can get a better understanding of your activity, position and influence within society by looking directly at your connections to other individuals and groups. A more traditional sociological approach might focus on your family unit, your membership of an institution or your position within a wider social structure, such as your class or your ethnicity. The internet has triggered a boom in social network science for two fairly obvious reasons. First, the networked infrastructure of the technologies upon which the World Wide Web is built has helped establish the idea that the underlying structure of human society is also networked. Second, because it has been quite easy to download large amounts of data from popular social media companies (until quite recently), it has been possible to visual and study ‘social networks’ in ways that appear quite compelling. The techniques for conducting this work are relatively straightforward. Before the Cambridge Analytica scandal, any third party could request access to your list of friends and then download that information from Facebook’s application programming interface (API). The API is effectively a web address that works something like a wall socket, allowing developers to plug into the massive grid of data that Facebook collects from its users. In response to that friend request, the API would serve up a long list of usernames—the same list that you see when you log in to Facebook and scroll through the names of your connections. It’s easy to imagine that list of connection visually, arranged like the spokes on a wheel

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Fig. 3.1  A simple one-degree social network

with you the hub at its centre. Everyone in this list is united by at least one attribute in common—they are your friends on Facebook (Fig. 3.1). On its own, that’s not a particularly interesting network, but having obtained your friend list, I can then request the equivalent list from every one of your friends. Let’s say that you have 10 friends in total (you have far more than that, I know) and every one of them also has 10 friends: now I have a list of 110 names and beside each name is a unique identifying number so that I can tell them all apart. This is important, partly because some people may have exactly the same name, but more likely because the list of 110 names is going to contain repeat appearances. After all, the chances are that you and your ten friends know some of the same people. It would be a very strange set of circumstances indeed if you were the only point of common connection between your ten Facebook friends. I begin to trace those common connections. If some of your friends are connected to each other, I draw connections between the spokes in the wheel. Then I look at each of the separate friend lists. For each new name I draw a new point on my graph. If that name appears in several lists, I connect it to each of those users. The more points of connection, the more centrally I have to locate the new name so that I can capture all those new connecting lines without losing complete control of my graph. If a name appears just once, by default it floats to the outer edges, like a kite on a long string hovering high above the fray. Quite quickly, my graph ceases to look anything like a wheel and becomes a tangled web—a network—in which the highly connected users are in the middle (Fig. 3.2).

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Fig. 3.2  A more complex social network featuring varying degrees of connection

This is a very crude description, obviously, but it captures the basic principles of network analysis: we trace connections between individuals and this allows us to picture these individuals within a wider web of association and influence. If you reflect on your own Facebook friend list, perhaps you are confident that you nestle right at the centre of the network—you are the hub of this spinning social whirl. Perhaps (like me) you know you are the kite, connected yes, but only by a gossamer thread unspooling rapidly. Social network analysis allows us to differentiate between friend lists and to recognise that popularity is more than just a number. It gives us a different perspective on our associations. All of which sounds pretty impressive, so what’s the problem with this approach? Well, assume for a moment that you are the kite. What’s more, the single connection you have to the rest of the network is someone you met at a conference five years ago—you exchanged Facebook details but haven’t spoken since. In one sense the network analysis is correct, you’re hardly central to what’s happening here, but in another it’s very wrong indeed: really you’re not part of what’s happening here at all; your appearance in this analysis is incidental. Alternatively, what if you are well connected in this group and find yourself in the middle of the graph. You’re a central actor in this network, pivotal to its dynamic, but in all honestly you don’t check Facebook as much as you used to and you can’t remember the last time that you posted anything. In these circumstances, what exactly does your centrality mean? How real is your pivotal role at the heart of this network?

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These scenarios are pretty contrived and social network analysis does have answers to these unlikely cases, but the issues being illustrated are pervasive, even in common scenarios. There are ways to distinguish between connection types. For instance, we can mark a friendship as being ‘one-way’ (weak) or ‘reciprocal’ (stronger). Certain graph analysis techniques allow us to attach attributes or values to the connect, so we can differentiate between strong friends and distant associates. Unfortunately, though, none of these techniques get close to capturing the complexity of lived human relationships and none of them capture the temporal variation in this complexity. Friendship on Facebook, at least as documented by the API, is something frozen, rigid and tasteless; unless you always immediately update the status of your friendships (an action than demands something from you), nothing changes. In many senses, the network we just drew exists outside of time, as an idealised form. As such, it’s a pretty poor approximation of anything we experience, as lived, in the world. This brings us full circle back to TV cathode-ray tubes and the intrinsic uncertainty of quantum relations and interactions. It is fundamental, empirical truth that we can only know something for the fleeting moment in which we interact with it. Outside of that time, we are estimating its existence. A social network is not a carefully drawn connective graph; it is a fluid and fluctuating assemblage, a probability cloud of associations, forming and reforming in time. We require methods that recognise this essential characteristic of reality—rather than imagine essences, we want to observe events: We therefore describe the world as it happens, not as it is. Newton’s mechanics, Maxwell’s equations, quantum mechanics, and so on, tell us how events happen, not how things are. We understand biology by studying how living beings evolve and live. We understand psychology (a little, not much) by studying how we interact with each other, how we think. … We understand the world in its becoming, not in its being. ‘Things’ in themselves are only events that for a while are monotonous. But only before returning to dust. Because sooner or later, obviously, everything returns to dust. (Rovelli, 2018, pp. 91–92)

This is the great strength of a systems approach to studying complexity: it does not recognise essences, it hardly differentiates between objects and it is fully focussed on interaction events that have some empirical status. It is like studying the rain by watching the surface of a lake, counting the

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splashes. The process tells us little about the nature of clouds, only hints and induced probabilities, nor much about gravity, nor the way the rain swirls and arcs on the wind—it tells only what happens when rain and lake meet, how each is changed and how those changes ripple outwards.

Note 1. Marx’s historical materialism, Durkheim’s social structuralism and Weber’s macro-sociology—all are attempts to grapple with the paradox of empirical order in a world of dynamical chaos.

References Appadurai, A. (1991). Disjuncture and difference in the global cultural economy. Theory, Culture and Society, 7, 295–310. Baldamus, W. (1982/2010). Networks. In M. Erickson & C. Turner (Eds.), The sociology of Wilhelm Baldamus: Paradox and inference. Farnham, UK: Ashgate. Barbour, J. (1999). The end of time: The next revolution in physics. Oxford, UK: Oxford University Press. Callon, M., & Latour, B. (1981). Unscrewing the big Leviathan: How actors macro-structure reality and how sociologists help them to do so. In K. Knorr Cetina & A.  Cicourel (Eds.), Advances in social theory and methodology (pp. 277–303). London: Routledge. DeLanda, M. (2006). New philosophy of society: Assemblage theory and social complexity. London: Bloomsbury Publishing Plc. DeLanda, M. (2016). Assemblage theory. Edinburg, UK: Edinburgh University Press. Deleuze, G., & Guattari, F. (1980). A thousand plateaus: Capitalism and Schizophrenia. London: Continuum. Deleuze, G., & Parnet, C. (1977). Dialogues (H.  Tomlinson & B.  Habberjam, Trans.). New York: Columbia University Press. Doebeli, M., & Ispolatov, I. (2014). Chaos and unpredictability in evolution. Evolution, 68(5), 1365–1373. https://doi.org/10.1111/evo.12354 Erickson, M. (2012). Network as metaphor. International Journal of Criminology and Sociological Theory, 5(2), 912–921. Heelan, P.  A. (2011). Phenomenology, ontology, and quantum physics. Foundations of Science, 18(2), 379–385. https://doi.org/10.1007/ s10699-011-9247-6 Karaman, O. (2008). A new philosophy of society: Assemblage theory and social complexity by Manuel DeLanda. Antipode, 40(5), 935–937. https://doi. org/10.1111/j.1467-8330.2008.00646.x

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Latour, B. (1996). On actor-network theory. A few clarifications plus more than a few complications. Soziale Welt, 47, 369–381. Latour, B. (1999). On recalling ANT. The Sociological Review, 47(1_suppl), 15–25. https://doi.org/10.1111/j.1467-954X.1999.tb03480.x Latour, B. (2004). On using ANT for studying information systems: A (somewhat) Socratic dialogue. In C.  Avgerou, C.  Ciborra, & F.  Land (Eds.), The social study of information and communication study. Oxford, UK: Oxford University Press. Latour, B. (2011). Networks, societies, spheres: Reflections of an actor-network theorist. International Journal of Communication, 5, 796–810. McCumber, J. (2011). Time and philosophy: A history of continental thought. Durham, UK: Acumen. McHarris, W.  C. (2011). Chaos and the quantum: How nonlinear effects can explain certain quantum paradoxes. Journal of Physics (Conference Series), 306. doi:https://doi.org/10.1088/1742-6596/306/1/012050. Nobus, D. (2002). A matter of cause: Reflections on Lacan’s ‘Science and Truth’. In J. Glynos & Y. Stavrakakis (Eds.), Lacan and science. London: Karnac Books. Orben, A., Dienlin, T., & Przybylski, A. K. (2019). Social media’s enduring effect on adolescent life satisfaction. Proceedings of the National Academy of Sciences of the United States of America, 116(21), 10226–10228. https://doi. org/10.1073/pnas.1902058116 Rickles, D., Hawe, P., & Shiell, A. (2007). A simple guide to chaos and complexity. Journal of Epidemiology and Community Health, 61(11), 933–937. https:// doi.org/10.1136/jech.2006.054254 Ritzer, G. (2012). Sociological theory (8th ed.). New York: McGraw Hill. Rogers, D. (2018). Assemblage theory and the ontological limitations of speculative realism. Dialogues in Human Geography, 8(2), 244–247. https://doi. org/10.1177/2043820617736623 Rovelli, C. (2018). The order of time (E. Segre & S. Carnell, Trans.). London, UK: Allen Lane. Russell, B. (1946). History of western philosophy. London: Routledge. Schrödinger, E. (1935). Die gegenwärtige Situation in der Quantenmechanik. Die Naturwissenschaften, 23(48), 807–812. Smolin, L. (2013). Time reborn: From the crisis in physics to the future of the universe. (Kindle Edition). New York: Penguin Books Ltd. Vaccaro, J.  A. (2018). The quantum theory of time, the block universe, and human experience. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 376(2123), 20170316. https://doi.org/10.1098/ rsta.2017.0316

CHAPTER 4

How Do Systems Work? Differentiation and Communication

It is time—past time perhaps—to pin down a definition of system interactionism, to specify the systems that are the focus for this book and to begin the process of describing how these systems interact with each other. The preceding discussion has produced five principles of interactive systems: . Systems are self-selecting analytical reference frames. 1 2. Systems at any level are material, social and symbolic. 3. Systems assemble when component sub-systems interact becoming more complex. 4. Interaction events happen in time. 5. Observation of interaction events is partial and perspectival. Any definition of system interactionism, then, must address each of these principles which, together, establish the ontological and epistemological foundations of the theory. A preliminary definition, therefore, might be as follows: System interactionism is the temporal study of discreet interaction events between complex systems within an analytical reference frame. This initial definition makes clear that the systems under study, and the interactions that any such study must attend to, are constructs of the reference frame through which the study is conducted. This, remember, is one © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_4

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of the central insights of the quantum model—superposition and relativity shape the system under study. Consequently, before we can begin any analysis, we must first define the frame, the systems that together constitute the frame and, additionally, establish exactly what our own perspective is within the frame. The frame, in other words, specifies epistemological priors, first by limiting the scope of our interest and, second, by reasserting that the interest itself shapes its target. After all, there are many questions that we could ask still of the systems we are hoping to study. What sort of systems are they, for instance? Do all systems work in the same way or are there different types of interactive assemblage? What is a ‘unifying logic’ exactly? Do these unifying logics order interaction processes, or do interactions happen at random, like Brownian gases colliding in a bell jar? If there are different categories of system, do we need different theories for them? How will these theories translate into methods? What are the implications for our disciplinary differences? In other words, while the previous chapter provided an abstract-­ theoretical discussion of what systems might be—and why that might be useful—we have said nothing yet about what systems must look like in the world, nor about how they must work, which is really essential labour if we hope to explore their influence. This book attempts to answer these types of question for a specific set of technological, political and symbolic systems. The next chapter describes the complexities of perspective, articulating the relationship between a specific set of methods and wider system complexity. Chapters 6, 7 and 8 describe (and limit) the constituent systemic phenomena relevant to the assemblage of macro system that I will call, hereon, digital-politics. Before that work can begin, however, it is still necessary to limit and contextualise the scope of this study within the context of systemic action. That is, we need to know how systems work because this effort is predicated on the argument that a systems model of assemblage and interaction can better explain the action of dynamic, complex phenomena. Can we hope to analyse the influence of software without first knowing the regressive complexity of all the material and social elements necessary for that software to exist? How do we even begin to isolate a discreet system frame for study? At what scale should we be attempting analysis? The number of questions quickly becomes overwhelming. The challenge in this chapter is to limit the number of questions that need asking by identifying what we know (or can assume) already about social systems,

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and more specifically about the role that communication plays in their assemblage. Ultimately, the systems theory being proposed here borrows heavily from the work of Niklas Luhmann, the German sociologist and philosopher, who most fully developed a social systems theory during the twentieth century (e.g. Luhmann, 1995). Through a considerable body of work, Luhmann demonstrated how systems thinking can be applied to social complexity and described many of the social systems that we may want to consider. He also characterised the logics of these systems in ways that will prove useful. In particular, Luhmann argued that the primary function of a social system is to communicate: ‘[T]he major category of systems theory is not action (as in traditional sociology), but the difference between action and communication’ (Schwanitz, 1995, p. 139). This is a characterisation with profound implications. Of course, the language of systems and, more specifically, the conceptual modelling that systems theory encourages have a long history that extends beyond Luhmann. I don’t intend to review that history but it is, perhaps, worth mentioning briefly how my reading of Luhmann’s work is situated within that broader context. Why, for instance, am I relying on Luhmann’s interpretation of systemic differentiation rather than, say, the general systems theory of Ludwig von Bertalanffy (1968) or Norbert Wiener’s cybernetic theory, especially given our interest in computing systems? The short answer to this question is that Luhmann’s systems theory aligns better with the fluid, rhizomatic account of assemblage that I proposed for a phenomenological interpretation of physical systems theory. That fluidity, the ebb and flow of systemic aggregation and disaggregation, is an essential dialectic of reality in my view. It is the tension that holds systems in relation to each other in the present moment. There is something about the more engineered or mechanistic system models that I find less convincing, not least because the focus on control tends to promote essentialism. If we are seeking an event-based, temporal model of assemblage, then we need more space in our theories for temporal chaos. The scope of Luhmann’s theory ranges widely beyond the concerns of this book. Many of his ideas align pretty closely with the current interpretation of system interactionism (although others are contradictory or less relevant to our concerns). For instance, the system’s self-constitution against its ‘environment’ is an idea that mirrors subjectivity in system interactionism, which is both local and perspectival, a product of partial access to the wider interaction cloud. Both ideas echo Husserl and Heidegger and a particularly Germanic ideal of self-constitution always in

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and through the world (Moeller, 2006; Paul, 2016). Still echoing Husserl, Luhmann argues that ‘[m]eaning enables both social systems and systems of consciousness to re-internalize the difference between themselves and their environment and to use it in organizing their relations with their environment’ (Schwanitz, 1995). Meaning—which is a term that will feature often in this chapter—thus plays several roles in the self-constitution and self-awareness of systems and should at least feature in a discussion of system logic and the organisation of assemblage.1 Similarly, in Luhmann’s account, systems define themselves through comparison against other systems in their environment. The term he uses is self-reference, which ‘establishes a method of theoretical self-control by comparison and self-observation’ (p.  141). This enables systems to differentiate between what is internal and what is external and to establish a boundary between these two domains. As such, knowledge of self, regulation of self and continuation of self are the system’s primary concerns, which explain why communication is the essence of systemic reality (e.g. Luhmann, 1990). This process of internal reflection, differentiation and external comparison could answer some of the questions posed earlier about types of system, logics of assemblage and so on. Significantly, Luhmann is clear that a general systems theory should be sufficiently abstract to be interpretable and applicable to different types of system. Matter, organisms, technologies and societies are different types of system, governed by the same abstract logics but, still, clearly and self-referentially different. System interactionism assumes something similar but not exactly the same. Luhmann’s systems have clear boundaries that differentiate subject from object, but system interactionism blurs these boundaries because systems are always interacting with each other. In one sense, this appears a profound contradiction but in another it is simply a difference of scale. Whereas Luhmann was responding to ideas from biology, neuro-biology and psychology, systems interactionism is taking inspiration from quantum concepts.2 It is trying to dig deep into system complexity in search of granularity, on the assumption that the mechanics of interaction will influence macro social effects. Luhmann was developing a social theory, seeking explanations for the visible, interrogating literature and the justice system. System interactionism is concerned with the subtle dynamics of affect and effect between technology and user. If system interactionism were not different from Luhmann’s theory then we would have no need for it. Much like ANT in the previous

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chapter, we would use the established theory and the book could proceed without these theoretical discussions. The important point here is that while Luhmann and Latour approach system/network dynamics from the macro perspective—from a world filled with objects and institutions, rules and behaviours—system interactionism approaches from somewhere else, from ‘underneath’, which is a very different place. System interactions ‘look’ different at the micro-scale. The systems themselves become ‘fuzzy’ interactive assemblages; existence is event-based and probabilistic; differentiation is only made between intra- and inter-systemic interactions, and then it is only a matter of shifting perspectives. It is a weird world but an intriguing one, and it comes with an entirely different toolkit for observation and experimentation. The hope is that these tools can be adapted to study complexity in larger scale systems3—not at Luhmann’s level, perhaps, but somewhere in between: the interaction between software and semiotics, for instance. The challenge in this chapter is to find the appropriate place to ‘meet’ on this scalar spectrum, to use Luhmann’s social system theory to guide a system interactionist methodology (up from the depths) towards a productive place for analysis. In simpler terms, we want to know if we can use quantum insights—both theoretical and methodological—to study complexity in the social world. In Luhmann’s social theory, difference, autopoiesis, communication and meaning are key concepts for explaining the improbable (remarkable) self-organising logics through which complex (social) systems create themselves from chaos. If we are going to use Luhmann’s work to interpret interactionism for social systems, then we need to engage fully with these concepts. First, ideas about difference and differentiation help establish the fundamental drive towards system assemblage. We can use Luhmann’s account of autopoiesis to engage with the self-organising logics of these systems, on the understanding that internal dynamics of system-­ organisation will construct the potential for external (environment-facing) interactions. Functional differentiation can help us locate, define and describe the social systems that are relevant to the types of question that we are seeking to ask. Second, we can recognise the essential symmetry between communication and interaction. Systems must interact to communicate and an interaction event is fundamentally communicative: there must be awareness, exchange and interpretation. Therefore, Luhmann’s analysis of meaning and communication can help us to develop a typology of system interactions and, from that typology, a methodology to direct our observation and analysis.

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Difference If we assume that the reality is a vast field of interaction events happening across a multitude of scales, then we must also accept that this is an inherently disordered and fractious state. Everything that we can observe— from the simplest materials to the most complex social institutions—is in some sense illusory because, ultimately, everything can (and someday will) break down into simpler, modular, constituent systems. There is nothing essentially unifying about the components in my iPhone. They existed materially before they were mined and manufactured, and eventually they will be broken apart, some of them will be reused and some will be recycled. The same is true, of course, of the cells and the nutrients that constitute my physical body—I know that they will disaggregate and break down eventually and that matter that I currently consider integral to me will eventually move elsewhere in the ecosystem. In these circumstances, why do we think of systems as cohesive units at all? Well, most obviously, because this is how they present to us at the scale at which we interact with them. Systems persist in time, looking much the same today as they did yesterday, and so somehow we must explain how this integrity arises. Ultimately, the reason is this: there can be no systems with difference— without a distinction between what the system is and what it is not. Luhmann’s phrasing of this fundamental logic sounds, by his own admission, quite paradoxical. ‘I thus begin with the claim that a system is difference—the difference between system and environment’ (Luhmann, 2016, p. 38). For Luhmann, the act of distinction is foundational: ‘Draw a distinction otherwise nothing will happen at all’ (p. 43). If something is to exist, then it must be able to differentiate itself from that which it is not. Regardless of scale, nothing happens without difference. This is the lesson that he draws from the calculus of George Spencer Brown and the logic that he identifies in the sociology of Gabriele Tarde. It is necessary for his own system theory and it is the root of complexity too. Complexity begins with a distinction between two states; it grows through the operation of further distinctions. It is this ordering that differentiates complexity from chaos (which can therefore be interpreted as distinction without ordering). Everything that makes a system originates in this initial act of differentiation. This is the sense in which a system is the difference between itself and other systems. In Luhmann’s social theory, the environment plays a crucial role, because this is what systems primarily differentiate themselves against.

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What is ‘the environment’ in systems theory? The concept is far from easy to define because it plays different roles in the explanation of how systems form. In one role, the environment remains pretty much undefined; it is simply that which the system is not. In this role it remains a nullity, an absence, defined entirely by not being the system. In another role, the environment is that which the system is able to observe, which does at least define it but only as a product of systemic operations (la Cour, 2006). If we attempt to apply this dual definition to a discussion of systems in the world, then we realise that it allows for a couple of different possibilities. In a world of many systems, the environment of any one system is everything that is not internal to the operation of that system. That means that the environment includes all the other systems (which the primary system can observe) and everything else too—all the non-systemic entities, whatever they may be. This makes a certain amount of sense and will be an idea familiar to many of us: we think of our environment as being everything but us. It includes our habitats and our climates, our material and our cultural surrounds. Perhaps we do not immediately think of it as including our families and our friends—colloquially we may think of ourselves sharing an environment with other subjects—but we do distinguish between ourselves and others and so, logically, those other subjects must belong to our environment even if we want to distinguish them within the environment. System interactionism says that everything is a system, which means that the environment must also be a system, because there is no space for it to be anything else. In effect, the environment is all the systems that are not the system of study. Why bother with this distinction? After all, system interactionism cannot escape from the logic of difference. Precisely because the environment is a system, it must be distinct from something that is not a system. Partly this is a semantic issue. System interactions begin with quantum systems and so begin with the language of these systems. The environment therefore becomes the sum of all the systems with which a single system can potentially interact. At full scale, or the fullest limit of interactive potential, the environment is the universe and the problem of what lies outside of the universe can be returned to theoretical physics and cosmology. Luhmann operates with a slightly different terminology, reflecting the theoretical lineages that are drawing from social difference, information theory, calculus and biological systems. There’s no need to dwell overlong on the definition of ‘environment’ because I don’t believe that the disagreement makes the two version of

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systems theory incompatible, and it has only a minor effect on the application of either theory. System interactionism fully accepts the origin of complexity in difference between self and other, system and environment or subject and object. It also accepts Luhmann’s argument that operations of difference increase system complexity. Both assemblages and actor networks come into being through difference. The significant difference—or, let’s say the point of departure, because quite how significant the difference is remains debatable—concerns the limit of the operations of difference that increase complexity. Luhmann says that only internal differentiation is possible. System interactionism contends that if we want to consider relationships of influence between systems, we might ask if external differentiation also takes place. In other words, it wonders if two systems might differentiate together for a while.

Differentiation and Self-reference Differentiation is the ongoing process of establishing and re-establishing difference in the world of interactive systems. In Luhmann’s social theory, difference and autopoiesis are key concepts for explaining the improbable (remarkable) self-organising logics through which complex (social) systems create themselves from chaos. He argues that this is the defining feature of social systems: they self-­organise, and reproduce themselves, with communication. But Luhmann makes autopoietic organization an attribute of systems, not of life. … Thus for Luhmann, consciousness and communication are not strictly speaking attributes of life but of systems; meaning and life are not attributable to each other. … It follows that social systems are not based on individuals, their perceptions or even acts; they are constituted in communication. … Individuals use communication to reduce complexity by creating a boundary between themselves and the outside world, and they are constituted through their act of communication with that environment. (Hartley & Potts, 2014, pp. 23–24, my emphasis)

Differentiation must begin somehow—a system must exist before it can operate, regulate and become more complex. How does it begin? It begins with a difference—a distinction—‘he who observes something must distinguish himself from that which he observes’ (p.  43). In other words, observation requires self-reference and there can be no systems without an

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environment and a boundary that distinguishes one from the other. All life, indeed all existence, presupposes this difference. According to Luhmann, autopoiesis happens in closed systems. In Luhmann’s sociology, there is no common language for communication between systems or between a system and its environment (Maturana & Varela, 1980). ‘On the level of its operations the autopoietic system does not receive any inputs from the environment but only perturbations (or irritations), which then might trigger internal operations in the system’ (Seidl, 2004, p. 3). This means that cause-effect questions are complicated across systems. For instance, the justice and the political systems do not communicate with each other directly, so cannot exert direct influence, though one system can be aware of changes in another, and so the law can respond to political changes through its own processes of differentiation. This seems to be somewhat different from system interactionism, which does not delineate absolutely between system and environment. How can we move from Luhmann’s position towards a blurred view of systems boundaries and the potential for direct communication? Well, the two positions are less contradictory than they seem. I made the point earlier that differentiation is partly dependent on scale—so it is not an ontological certainty. At the quantum scale there is constant interaction between systems. Communicative specialisation happens more in larger scale systems: the languages of law and politics have higher-order differences, but the macro-structures are built on mutually interpretable foundations. To a large extent, communication between systems can be revealed if we dig deep into the details of systemic assemblage. Luhmann’s systems are also event based. This point needs some clarification because it illustrates the general compatibility between Luhmann’s social systems theory and the development of system interactionism through an event-based assemblage model. According to Luhmann, systems must be constituted from events because only events have ‘their transitoriness and their continuous disintegration’, which is necessary to generate the ‘indefinite complexity’ required for complexity to assemble (Schwanitz, 1995, pp. 146–147). From the wild dance of events, there emerges an ordered pattern of expectations that serves as a means of orientation for the events. In systems theory, this concept is to be found in many variations. For example, complexity and contingency bring about their reduction; the potential for continuous conflict and mutual misunderstanding brings forth the rules for their avoidance;

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the unpredictability of other peoples’ behavior generates expectations; the impenetrability of conscious- ness (even for consciousness itself) necessitates communication. The stability achieved by these means is of a dynamic nature. It needs to be fed constantly with fresh disorder, fresh dissent, fresh unpredictability, and fresh impenetrability, if it is not to collapse (Soziale Systeme ch. 8). (p. 147)

Like assemblages, systems are temporal and kinetic. There is constant churn, a cycle of aggregation into disaggregation into reaggregation, which is necessary to produce the diffuse possibilities required for ever-­ increasing complexity. In the previous chapter I described this as a dialectic between chaos and complexity. ‘For the system, the advantage of surrendering to the transitoriness of events lies in the fact that events finally disintegrate, thus enabling the system to replace them by events that are better adapted to the environment’ (p.  147). I have not yet attempted to describe the ordering logics by which systems assemble, leaving a gap in the theory for exploration in later chapters, but Luhmann does provide an answer. He claims that there are two possible forms of establishing order: through processes and through structures. These logics are entirely mechanical, meaning that they specify only the logical conditions of interaction and have nothing to say about the forces that might impose on those logics. A process is an event chain—an irreversible series in which each event establishes the conditions and the variables for the next; structures form from processes that are repeatable and therefore more probable. It is only with time and through operation that the system becomes itself. ‘The system creates itself as a chain of operations. The difference between system and environment arises merely because an operation produces another operation of the same type’ (p. 46). This is what Luhmann means by autopoiesis: the circular operations of self-production, which begin with self-reference and become more complex. It is only with autopoiesis that the environment can play the second role assigned to it: it can become that which the system is able to observe. This is a crucial point. The environment is constructed through the system’s ability to observe it. That means the environment depends on a system’s observational complexity, which depends on its internal complexity. Not all systems are the same. It’s worth pausing to consider how these ordering logics might appear in practice. Concepts like ‘process’ and ‘assemblage’ can feel pretty

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elusive, especially in abstract discussion. They become easier to grasp when they are applied to practical examples and this, after all, is a necessary step if we intend to use them to ground empirical study. Luhmann begins with systems composed of ‘elements’ but translates these elements into events, reasoning that elements must be time-bound in order to generate sufficient complexity for systems to begin to assemble. This might seem like a substantial conceptual leap, and a little confusing, but really it is the same logical step that we made in the previous chapter. Systems differentiate themselves, which means two things. First that systems change and, second, that the change comes from within, it is self-propagating. If the elements that made systems were static, if they were essences outside of time, then the only possibility for system-change would be the reordering of elements in relation to each other, and even this reordering is difficult to imagine without elemental change: what would drive it? Systems would be locked into a finite number of elemental combinations. There could be no evolution and no knowledge growth; complexity could be reordered but it could not always increase. So system elements must exist in time, which means that they are events by definition. They may be stable, long-lasting events (like a stone) or violent, short-lived eruptions (like a lightning strike), but they are all temporary associations of materials and forces. Of course that means that they are all also systems but Luhmann is focussed on a social theory rather than an ontology. Change within systems is driven by change in these elemental associations. The aggregation and disaggregation of elements is analogous to mutation in cellular reproduction—it creates difference for selection and recombination. Autopoiesis originates as a biological metaphor. System interactionism advances the autopoietic model because it explains how elemental change happens. It is a little unsatisfying, I think, to argue that elements must be events because they need to be events; otherwise systems cannot change. It is far more satisfying if we can explain why that change happens. It happens because systems interact with each other. The model is recursive. Systems change because they are interacting with other systems in the environment and because, internally, component sub-systems are interacting with each other.4 Each sub-system is subject to the same dynamics, changing itself and how it interacts externally, because it too is composed of sub-systemic assemblages. What happens at a larger scale, though, in social systems where Luhmann’s processes and structures operate? First, elements that change become linked. That means that one element changing influences another

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to change, which in turn influences another. There are obvious biological examples we can use to illustrate these ideas, various cellular pathways and chain reactions, but the process is meant to work the same in social systems too. We might reflect on the way that a court hears evidence from both prosecution and defence before a jury decides, or how certain societies develop potlatch exchange rituals, establishing expectations and familiarity from a climate of mistrust and conflict. Digital media systems have processes too. Consider the act of composing and publishing a tweet, an act performed by millions of accounts multiple times each day. When a tweet is published by an account with many followers, several things almost certainly happen. It is seen widely when other accounts interact with it, responding with comments or republishing it via retweet; it reaches other influential Twitter uses who react in turn; the Twitter algorithms record its popularity and promote it further, a conversation grows around it, a trending topic forms; users respond to this popularity, and follower numbers increase. One event follows the other with a certain predictability and, in this way, networks of influence develop, discourse communities grow and algorithms reorganise their parameters. In this way, events become processes, from which structures arise: structures of power, semantics and code. In previous chapters I have referred to the idea that social media operates according to certain repetitive processes, which van Dijck and Poell (2013) have called ‘social media logics’, namely programmability, popularity, connectivity and datafication. What are these logics if not structures of events—technicities—predictable, repetitive, defining parameters according to which the system takes shape? Popularity is differentiated on twitter through a series of events that spread tweets, link accounts, produce data, all of which feed into a program that feeds back into increased popularity: different internalised structures interacting to shape the Twitter system. This realisation has important implications for the empirical observation and analysis of systemic assemblage. If we want to better understand how a system reproduces itself, how it takes shape according to its own logics of differentiation and reorganisation, then we need to search for the events that become processes that become structures. In practice, that means that we must begin with the dominant structures, identify the processes through which those structures form and then deconstruct those processes to reveal the events of influence. What will those events look like? They will involve interactions between component sub-systems

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(elements); they will be common and therefore predictable; and they will create further events in ways that are also predictable. In other words, we will assign influence to interactions that generate more interactions and do so repeatedly.

Functional Differentiation If everything is a system, if interactions are everywhere, how should we know where to look for the interactions that we need to research—those that are relevant to our questions of cause and effect? This is not an easy question because there are profound complications from assuming that rather than being discreet and self-perpetuating entities, systems blur into one another. It becomes difficult to isolate cause and effect in any meaningful way—to suggest, for instance, that the influence one system has over another is experienced by that system alone. If we imagine that reality is a vast landscape of interactions events, then in some sense all events are connected. The effects of a collision here will eventually ripple through that landscape to be felt far away and those consequences may be nigh on impossible to trace, let alone predict. This is the butterfly effect, of course, Edward Lorenz’s famous and evocative illustration, in which a butterfly flaps its wings in one place and changes the course of a tornado in another (Ghys, 2015; Lorenz, 1963). The model—it’s not a metaphor exactly—is meant to illustrate how the smallest, most subtle events in one place can prompt a series of changes that eventually impact elsewhere in the system in unpredictable ways and on entirely different scales. It’s pretty easy to identify problems of this type in our social world and it seems pretty clear that our social thinking is not well adapted for this type of complexity. Take any example, really, whether it’s a small change in a government policy or a vast epoch-defining challenge like climate change, and it’s evident that we are taking decisions without fully considering the potential consequences. How can we? After all, the point of the Lorenz’s illustration is not that the butterfly will cause a tornado but rather that it is impossible to know if it will because the cause-effect chains in interactive systems are so complex and chaotic. That’s not the point though: not knowing is not the problem. The problem is that we assume that we can ignore the complexity, and we can make confident predictions and engineered decisions, imagining the world to be far simpler and far easier to tame than it is.5

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In theory, there are two possible responses to the problem of complexity-­ ignorance. The first is to engage fully with the reasoning—and, ultimately, with the mathematics—of chaos and complexity theory. Social systems theory needs to develop in tandem with our growing understanding of systems probability, computer assisted modelling and variable regression techniques. Ultimately, this is the path that systems interactionism should take—the project is most likely to become an effort to limit social knowledge or, at least, an effort to describe the uncertainty inherent in social knowledge. This is not meant to diminish it, however, because recognising general uncertainty is a necessary precursor to delivering specific certainties. In truth, though, the full integration of complex social and systems theory is still some way in the future. Ambition here is more limited. The second possible response to the problem is it to impose an artificial frame on the systems under consideration, which is an idea already implicit in the theory. Working with self-selecting analytical reference frames, we can search for interaction events that exert immediate and dominant influence across the systems under consideration. We imagine that our systems are isolated even if they are not, and we seek cause-effect relationships that ‘rise’ above the background noise of system fluctuation and perturbation. In other words, we pre-select system dynamics that are meaningful to us rather than searching for meaningfulness in absolute (and far more widely distributed) systemic terms. After all, when I strike a nail with a hammer, the fact that the impact may echo through eternity doesn’t change its immediate consequences: the nail is driven into the wood through mechanics that are entirely more predictable. Luhmann’s macro system theory can help us select these reference frames. If we accept the logic of a functionally differentiated society then we can begin to map the systems that we have pre-selected as meaningful in a given context. According to Luhmann, society is structured into discreet and clearly differentiated systems; for each system, other systems form part of the environment in which that system self-perpetuates and regulates (Mattheis, 2012). ‘In relation to the system, the environment may be regarded both as a complex web of other system-environment relationships and as unity’ (Schwanitz, 1995, p. 143). These discreet systems are functional specialists, so while the legal system can only consider a problem as a legal problem, it does so with the full weight of its differentiated experience and expertise, thus further perpetuating the ‘complex structured unity of a system’ (p. 630).

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What’s crucial to recognise, here, is that meaning is partly a function of ‘the way things are’ but also a function of where we focus our attention. The logics of self-selection involve a degree of interpretation. We can be guided by Luhmann’s system taxonomy while recognising that our own questions may require a shift in focus—a slight reframing of systemic emphasis. Luhmann wrote prolifically throughout a long career and produced several studies of these discreet functional systems, including books on the law, economics, literature, mass media and politics (e.g. Luhmann, 2000). In these studies, he explored the historical evolution of these mega-systems through autopoiesis. Crucially, the adaptation of evolutionary science for social study produces studies that are fully and fundamentally temporalised. The act of explaining a system becomes an exploration of its origin and development in time, through repeated cycles of differentiation, selection and stabilisation. Evolutionary logic is used to ‘reverse-­ engineer’ the complexity that seems most meaningful in the world around us. For Luhmann, as societies grow larger and more complex, the mechanisms for differentiation change so that in modern society differentiation no longer occurs between people but between modes of communication. The logics of functional differentiation help us to establish that there are different types of system and that these different types have developed different types of internal communication. Before we can study a system we need to be fluent in its peculiar language of internal differentiation. Moreover, if we want to study interactions between systems, we need to be multilingual. The examples raised in the introduction sit at a nexus between systems: technology (internet architectures, data structures, software), media, communication and politics. Following Luhmann, we will need a range of interpretative skills, fluency in a range of systemic langues or media codes (Luhmann, 1995), to have any hope of accessing, interpreting and critiquing these interactions. Most simply, to ask questions of these systems, we are going to need to ‘speak’ different languages: network protocols, code, political economy, media theory and critical textual analysis.

Communication The biggest difference between Luhmann’s system theory and the one being advanced in this book is that system interactionism assumes that both internal and external system differentiation is possible. I’ve suggested that this is partly an issue of size—it makes little sense to deny external

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interaction at sub-social scales, but it is also a necessary conclusion from recursive system logic. If a system is generated from its interacting sub-­ systems, then those sub-systems are engaged in external interaction—the boundary between internal and external is purely a product of perspective within the interaction field. Why does Luhmann close systems to each other in the way that he does? What are the implications of this decision for studying relationships between systems? Can we reconcile the differences between Luhmann’s position and a proposition that relies on unrestrained interaction? If we can answer these questions and, indeed, reconcile a social systems theory with a full interactionist perspective, then the benefits should be obvious. The previous section illustrated how a combination of the two approaches could ground a conceptual framework for identifying and studying influence within systems but we still need to articulate a framework for relationships between systems. Communication will be central to this discussion. I have described already how Luhmann uses communication to explain how systems are able to self-differentiate but rejects a common language for communication across systems. He argues that in modern societies the primary mode of differentiation is not between people but between modes of communication. This means, effectively, that major social systems are a little bit like major nation states, isolated and speaking different languages, but still largely aware of what others are doing and primed to react and respond. This seems somewhat paradoxical at first—why is the legal system ‘open’ to information about the political system but closed to information directly from it? Why is this necessary theoretically and how does it work practically? There are two types of answer to these questions. One was given earlier, which is that Luhmann is focussed on differentiating social systems in modern societies, but he is not trying to write a universal theory of system mechanics. Indeed, Luhmann’s systems theory is explicitly an adaptation of biological and ecological systems models—he uses the underlying framework of systems thinking to interpret macro social differences. Moreover, he is quite clear that differentiation through communication is a feature of modern societies, and suggests that ‘archaic’ civilisations worked quite differently.6 As such, differentiated communication is a feature of only some types of system and therefore may not be an issue if we research different systems from Luhmann. There are a couple of problems with this answer, and the first should be very obvious. We are interested in the same types of system as Luhmann. We may not be looking to diagnose

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‘the media’ or ‘politics’ in quite the same way, but we are clearly still dealing with social systems. The second problem is that this answer doesn’t fully engage with the mechanical logics of the question—it says that macro social systems are different but doesn’t explain why they are different. Another way to answer these questions, then, is to engage directly with the role that communication plays in autopoiesis, and to try and understand both why communication works the way that it does in Luhmann’s social system theory and to consider this role alongside a more interactionist approach. Taking this path, it soon becomes clear that Luhmann’s systems are not completely closed to each other, isolated by peculiar modes of communication, and that an interactionist approach should be greatly enhanced by a careful engagement with Luhmann’s autopoietic communication model. The first thing to accept is that while systems are self-reverentially closed, they remain ‘open’ to their environment, which includes other systems, in various ways. Recall that self-referential closure explains the development of complexity from chaos—it is Luhmann’s solution to the tension between empirical order and theoretical disorder. As I’ve just re-­ emphasised, it’s an attempt to explain systems at a specific scale of analysis. At that same scale, these systems are clearly not completely isolated from each other. It is demonstrably apparent that legal systems and political systems are ‘open’ to each other in both theoretical and practical terms. Politics decides legislation that is enacted through law. Hartley and Potts (2014) liken autopoiesis to an idea they call ‘externalism’, which posits that individuals are constituted through their relationships with other individuals and with the environment. Autopoiesis, then, is ‘the process of continuously reproducing identity by filtering meaningfulness from the external environment through communication’ (p. 23). Social systems are founded on communication and communication produces meaningfulness for systems from the external environment. How does this work? Systems learn about their environment and they learn from their environment—they extract meaning from it. What is meaning? The concept is common but it is often used in different ways, which make it problematic for many theorists. In Luhmann’s sociology, meaning is the difference between what is actual and what is possible. ‘Anything actual is meaningful only within a context of other possibilities’ (Schwanitz, 1995, p. 149). This is quite an obscure formulation. More commonly, meaning is defined in semiotic terms; it ‘refers to the sense of significance of a word, phrase of

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utterance, or in general, a sign’ (Hartley & Potts, 2014, p. 128). What’s important to recognise in this definition is that meaning is assigned—significance is attributed to any given sign and it is done within a wider sign system, that is a taxonomy of other signs and their associated meanings. In some sense, then, meaning is really a product of attention—in an infinite array of possible associations, what is meaningful depends on where we assign our attention. This idea is central to Luhmann’s definition of meaning. An environment holds abundant possibilities—an infinite number of events. If a system has no way to distinguish between events, to select some ahead of others, then there cannot be logical differentiation—there is nothing but noise. It is this selection process, the recognition of some events ahead of others, that elevates possibilities into actualities. Meaning is specificity in a world of abundance. Systems ‘filter’ their environments for elements that are meaningful to them. For this filtration process to work, if systems are to self-reproduce through their environments, then they must be able to differentiate between internal elements and external elements. For autopoiesis to function in human society, it is necessary to reconstruct, as operational differences in the self-­ constitution of the system by self-observation, the entire string of distinctions between system and environment, complexity and reduction of complexity, unpredictability and expectation, and order and disorder. As a consequence, the observing and the observed elements of a system must be set at different levels. (Schwanitz, 1995, p. 149)

Somehow systems need to be able to tell the difference between themselves and their environment; they need to recognise the boundary that their functional differentiation constructs around them. How do they do this? ‘At this point, Luhmann leaves the path of traditional sociology by defining the observing elements of the social system as communications, whereas the observed elements are defined as actions’ (p. 149). All social systems are formed through communication. ‘Communication can be considered as the basic unit of observation for the assessment of the operations of the social system’ (Mattheis, 2012, p. 628). This means that all social systems have the ability to observe themselves in action and to recognise that what they are observing is an integral part of themselves. Action is the basic unit of self-constitution.

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Individuals use communication to reduce complexity by creating a boundary between themselves and the outside world, and they are constituted through their act of communication with that environment. In doing so, they also construct the outside world through which they know themselves. (Hartley & Potts, 2014, p. 24)

The relationship between communication and meaning becomes clearer: communication is what systems do, meaning is what systems produce. We can phrase this differently and more completely: systems produce meaning for themselves through communication acts that assign significance to specific ‘actualities’ (events) in an environment of abundant possibilities. The task of aligning Luhmann’s theory with system interactionism is also clarified: we need to decide if communication acts are equivalent to interaction events. If they are, can we adapt a theory of communication acts to inform our analysis of system interactions?

Same but Different The issue we face is that Luhmann’s social theory differentiates clearly between communicative systems and their environments, whereas system interactionism posits a diffuse ‘cloud’ of fluid systems and possible interactions. Luhmann’s work begins as an attempt to explain social order and is specifically a response to the problem of the Cartesian subject. In effect, he is trying to explain how the world could have become as complex as it, without a ‘divine’ mind to order its becoming. It is a post-humanist endeavour. It does not assume that society begins with individual humans: ‘[F]or the first time, society is no longer regarded as the sum of its parts, but as a combination of system-environment differentiations’ (Schwanitz, 1995, p. 144). The fact remains, however, that Luhmann is preoccupied with social systems. His theory is nuanced and complex, compelling and provocative, but it prioritises society; it is principally concerned with this collectivised condition of being human. That is no reason for criticism because that is the entire point of social theory, but this social starting point does shape the requirements of the theory: it necessitates that Luhmann’s systems behave in certain ways. We are interested in social questions here too, of course, but we began with a quantum account of physical systems and a framework centred on interaction.7 That doesn’t make the systems here different from Luhmann’s but it does allow us a different perspective on the systems that we

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traditionally class as social. We might say that system interactionism is marginally more post-human because it does not elevate social systems as special or different in the total field of possible ordering events. In general systems theory, systems can be both opened and closed. An open system is one that allows interaction between its internal elements and the environment, whereas a closed system does not allow these external interactions (Bertalanffy, 1968). Luhmann does not deny the possibility of open systems, but he argues that social systems are a particular type, which differentiates through communication and must therefore be closed: Communication remains an internal operation. It never exits the system, for the next connection is provided for and has to take place in the system. Self-­ reference (reference to that which takes place in the system) and external reference (reference to the intended internal or external, past or present states of the system) must therefore be distinguished: one is utterance, the other is information. (Luhmann, 2016, p. 50)

In other words, it is differentiation through communication that necessitates closure because the communication act requires that there is a distinction between information and utterance. The system is the difference between system and environment in the same way that the sign is the difference between signifier and signified. It should be noted that Luhmann’s insistence on operational closure in social systems is not universally acknowledged (e.g. Varela, Maturana, & Uribe, 1974) and the insistence on system closure is not necessarily something that should derail a discussion of system interactionism. Systems are closed because self-observation demands a distinct boundary between self and other. Ultimately though, even given their closure, social systems are still able to inform and influence each other, just not through direct transfer. It is a curious and somewhat convoluted dynamic. In the previous chapter, I argued that interaction generated complexity and that interactions between sub-systems influenced the production of complexity in the system as a whole. The implication was that interaction can be material, social and textual, because this is how Deleuze and Latour characterise their assemblages and actor networks. It’s not wholly clear how this works in a society functionally differentiated through communication, however, especially if we assume that everything is a system. There appears to be a logical problem for operational closure and for functional differentiation—once intra-system elements are recast as being systems themselves,

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they must be both open and closed at the same time. How is this meant to work and, more specifically, what is communication in this dynamic? The key distinction that Luhmann makes between communication within systems and external-facing ‘observation’ relates to the production of meaning. Individuals and organisations are both clearly capable of communication, but meaning is only ever fully accessible within a system. In any relationship outside of that system, whether it’s two individuals meeting or whether it’s an advocacy group suing a government in an international court, observation of meaning production is possible but not has direct access to the meaning produced. That is because within social systems communication is bound by codes, culture and specialised media that are part of self-realising communication and not shared between systems. This is the sense is which a system is communicatively closed—any interaction between systems will always be ‘processed’ differently by the respective systems; it will mean something different to them both. In his later work, Wittgenstein (1922) wrote of language games referring to the interplay of imprecise meanings that can attach to signs within a semantic system. While de Saussure imagined a universal system for the formation, organisation and operation of language, its manifestation was culturally specific: ‘The culture and its needs determine the categories of meaning’ (Lewis, 2008, p. 12). The langue, to use de Saussure’s term, ‘is inevitably bound to the social and cultural context in which the language parole (specific utterance) is operating’ (Lewis, 2008, p. 113). This complexity was elucidated by Roland Barthes, who argued that meaning accumulates over and above structuralist signification through a process he called connotation—essentially the context-specific layering of meaning on top of an original association (Barthes, 1988). Communication is more a process of interpretation (and transformation) than transmission. ‘What are called distortions or ‘misunderstandings’ arise precisely from the lack of equivalence between the two sides in the communicative exchange’ (Hall, 1980, p. 131). Like these other theorists, Luhmann dispenses with any sense of direct sender-receiver transfer in communication in favour of an interpretative and uncertain semiotic struggle. According to him, communication is made possible by a combination of three different points of reference for selectivity, the first being information about the world, the second whatever the utterance betrays about the speaker, and the third being the understanding by the addressee. (Schwanitz, 1995, p. 150)

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Each communication act involves a negotiation to produce meaning from information, and the act is situated within a system; information is made meaningful internally—externally it is simply more information that must be observed and interpreted. All information is processed in this way by systems, all signs, all stimuli—this is not something specific to the written or spoken word. According to Luhmann, it’s not something specific to human beings or to consciousness either. Social systems and psychic systems process meaning in the same way through communication. Is there any difference between a communication act and an interaction event? Is communication a specific type of interaction, perhaps? Does a system interact internally through communication and externally through some other dynamic? If so, what is this other dynamic and why should we not call it communication as well? We don’t have answers to these questions yet, and the coming chapters of this book are largely an attempt to explore how internal and external interactions work in digital media systems. We do however have a much clearer idea of what the internal dynamics of systems might look like, of what self-selection might involve and of how self-actualisation might work. Social systems differentiate through communication and that communication is bound by system-specific logics—codes, programs and media that repeat certain processes and form certain structures, producing meaning from both internal and external information flows. What’s left to decide in this chapter is how far these communicative logics extend. Is there a fundamental difference between the social domain and the rest of the system-world? Really this is a question about definitions and in particular how expansive we believe that we can make the meaning of communication. Do stones communicate? I began talking about stones because the physicist Carlo Rovelli uses the stone to illustrate how some events are particularly long-lasting and stable—they hardly seem like events at all, so different is their scale, so alien is their stability given our fleeting human perspective on history. Systems are simply clusters of events in the gradual unfolding of the cosmos—their apparent persistence is always a matter of perspective. So we cannot use the stone’s apparent inertia to deny it communicative capacity, but can we describe any way in which a stone can communicate? It’s difficult, of course. Stones are constituted from material components, held together by strong but slow acting forces. They do interact with their environments, gradually shedding material components, their internal bindings slowly loosening, but still clearly remaining stones, at

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least until they become dust. So they share superficial similarities with our social systems, but that’s not the same thing is it? Does it make any sense to extend systems theory to a stone? In what possible way is a stone self-­ actualising its own ‘stone-ness’ through processes of observation and communicative differentiation? It’s obviously a little silly to be questioning the communication abilities of a stone. That’s not to say that there aren’t plenty of fascinating questions to ask about a stone’s ability to perceive itself—I happen to be particularly interested in ideas about consciousness and its dispersal through the animal and material worlds, and particularly its relationship to complexity, but the questions here are not about consciousness. A system does not have to be conscious in order to communicate; it just needs to engage in communication acts. Consequently, the limits of communication, especially in material or inanimate systems, really are something that we need to consider. We are researching technology after all: wires and servers, software and screens—if we think that these technological systems interact with humans in their societies, then we need to be confident that they have the capacity to do this. So how do we decide if a stone, a silicon processor or a piece of code can communicate with itself and with its environment? Ultimately, we have to decide if it can produce meaning. I was a little imprecise earlier when I said that these questions depended on the looseness of our definition of communication. Communication is the mechanical process—the business of observation, interpretation and synthesis—and so it retains quite a narrow definition. It’s the definition of meaning that becomes more expansive. As long as an event produces meaning itself, then it can be considered a system according to the principles described here. As long as it contributes to meaning production in a wider system, then it can be analysed according to the principles of communicative interaction. Recall that meaning is the assignment of significance in a world of abundant possibilities; it is an attribution process for making sense of stimuli. Meaning-making systems are attention managers—they act on massive data flows, extracting, simplifying and organising that data, producing sensible structures according to their own logics. Surely this is exactly what a computer does too. Digital technologies are systems because systems process information and assign significance, which is exactly what these technologies do.8 That means that we can use Luhmann’s account of internal differentiation to study the internal logics of digital systems, and we can use interactionism to explore the systemic influences of a

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technology. When the differentiation logics of one system begin to influence the differentiation logics of another, then we are dealing with relationships of cause and effect, and we are doing so with full awareness of the attendant complexity. This is a convoluted way of saying that we can apply the same interpretative logic to both communication acts and interaction events, as long as those interaction events involve systems that process information to produce significance, part of a wider process of building complexity in the world. It’s also a way of saying that we need to treat interaction events between systems as though they were communication acts. Rather than assume an ‘effects’ model of interaction, involving direct transfer and predictable cause and effect, we should recognise the situated processing of information (interpretation) and the probabilistic nature of any outcome. This logic is extendable to physical systems and material exchanges. By definition, any interaction can be interpreted from at least two different perspectives. The problem of perspective is considered in the next chapter and is significant because it presents considerable challenges for the empirical researcher seeking an ‘independent’ Archimedean standpoint from which to record her observations. As the philosopher Huw Price explains early in his discussion of quantum time, our present view of time and the temporal structure of the world is still constrained and distorted by the contingencies of our viewpoint. Where time itself is concerned, I claim, we haven’t yet managed to tease apart what Wilfred Sellars calls the scientific and manifest images—to distinguish how the world actually is, from how it seems to be from our particular standpoint. (Price, 2010, p. 5)

In short, any observation is a complexity of relations. Even if we are only attempting to observe a single interaction between two simple systems, we must still consider the event itself and the situated interpretative perspectives of the two systems, and we must consider how our own perspective shifts across that tripartite dynamic. Moreover, our observations will be processed through our own internalised system logics. Our empirical perspective shapes what we observe but also how we observe it (if the only instrument that we have is a pair of scales, then we will interpret everything by weight). This is what it means to embrace the complexity of systemic assemblage. Once we reject objective unity in favour of shifting

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event fields then we must accept that the knowledge we produce will be fundamentally different. In many respects, it becomes quantum, relative and probabilistic, dependent on clearly articulated priors or simplifying preconditions. Such a state may not seem appealing but I maintain that it is better to recognise complexity and be uncertain than it is to be certainly ignorant.

Notes 1. A discussion (let alone an explanation) of consciousness is clearly beyond the scope of the current work, though it is worth noting that system interactionism treats consciousness (like everything else) as a product of interaction between systems. A precise formulation for consciousness is not yet written but it is likely to include a couple of key elements. Consciousness must surely be the product of intra- and inter-systemic awareness—that is the ability to attend to the difference between internal interactions (interactions that generate ‘me’) and external interactions (interactions between me and the world). As such, consciousness is certainly not restricted to human beings and, most likely, follows a pan-psychic logic, in which any system of sufficient complexity is able to generate a degree of consciousness. Of course, it is unknown exactly what level of complexity might be sufficient. Logically, this is largely the same formulation that Luhmann borrows from Husserl. 2. This ‘blurring’ at the boundaries of systems recalls deliberately Boltzmann’s explanation for how time-symmetry (at the atomic level) translates into asymmetry at the macro level, where the second law of thermodynamics applies. The seemingly irreversible ‘arrow of time’ is therefore tied to entropy, which only ever increases (for which, read complexity in this account). The blurring metaphor captures something essential about the limits of our descriptions at the macro-level: ‘[E]ntropy depends on the system’s microstate but also on the coarse graining under which the system is described. In turn, the relevant coarse graining is determined by the concrete existing interactions with the system. The entropy we assign to the systems around us depends on the way we interact with them—as the apparent motion of the sky depends on our own motion’ (Rovelli, 2017, p. 285). 3. Especially if those larger scale systems produce enormous quantities of granular, self-referential data. This type of approach becomes possible because of enormous computing power and expansive data capture, the products of surveillance capitalism. 4. The reader may wonder, why should systems interact? Is this not just a deferral of causality? Ultimately, interaction is driven at the molecular and sub-molecular by forces that are described in physics and in chemistry. At

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some level, interaction reduces to sub-atomic collision. Eventually, we descend to the level of elementary particles, of quarks and strings, where we must hypothesise different dynamics, for now at least. 5. Many people have criticised internet companies, especially, for having exactly that mindset (e.g. Morozov, 2013) but, in truth, it’s typical of a globalising culture of which Silicon Valley is but a part. How do we describe this culture? It is global, yes, but most likely western in origin, born from capitalism, perhaps, and an alienated professional class that turned its labour back in and on itself. It is inscribed in a knowledge economy that cannot value uncertainty but fetishes mechanisation and speed (Harvey, 1990). It loves data and thinks sometimes like a software engineer and sometimes like a market trader. It urges more datafication, more automation and more capitalisation. 6. Differentiation is a feature of modern society only according to Luhmann. Earlier societies featured other types of differentiation and these different types persist into modernity as well. What does this mean? Effectively, it means that modern society is differentiated in different ways. As well as macro-functional systems (politics, law, medicine) there are organisations that operate within these functional categories and there are short-lived ‘interactions’, that is person-to-person contacts of both formal and informal types. In other words, modern society involves all different types of individuals, groups and institutions communicating with each other within functionally differentiated systems. 7. An interaction is a distinction too, an event that necessitates a null time without events. An interaction is the difference between presence and absence or, as we shall discuss in the next chapter, a redirection of observer attention, a stimulus that initiates a perceptual response. 8. We need to be a little careful with the circular logic here. It is easy to get lost. Meaning and communication are bound together in a tight referential cycle. Communication acts produce meaning; meaning is the product of communication; systems are systems because they produce meaning through communication, always becoming themselves. For Luhmann, society is defined through the continuation of communication; it is ‘always becoming’ itself.

References Barthes, R. (1988). The semiotic challenge (R.  Howard, Trans.). New  York: Hill & Wang. von Bertalanffy, L. (1968). General system theory: Foundations, development, applications. New York: George Braziller.

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Ghys, É. (2015, 2015//). The butterfly effect. Paper presented at the Proceedings of the 12th International Congress on Mathematical Education, Cham. Hall, S. (1980). Coding and encoding in the television discourse. In S.  Hall, D. Hobson, A. Lowe, & P. Willis (Eds.), Culture, media, language: Working papers in cultural studies (pp. 197–208). London: Hutchinson. Hartley, J., & Potts, J. (2014). Cultural science – The evolution of meaningfulness. London: Bloomsbury Academic. Harvey, D. (1990). The condition of postmodernity: An enquiry into the origins of cultural change. Oxford, UK/Cambridge, MA: Blackwell. la Cour, A. (2006). The concept of environment in systems theory. Cybernetics & Human Knowing, 13(2), 41–55. Retrieved from https://www.ingentaconnect.com/content/imp/chk/2006/00000013/00000002/art00004 Lewis, J. (2008). Cultural studies – The basics. London: Sage. Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20(2), 130–141. https://doi.org/10.1175/15200469(1963)0202.0.CO;2 Luhmann, N. (1990). Essays on self-reference. New York: Columbia University Press. Luhmann, N. (1995). Social systems (J. J. Bednarz & D. Baecker, Trans.). Stanford, CA: Stanford University Press. Luhmann, N. (2000). Art as a social system (E. M. Knodt, Trans.). Stanford, CA: Stanford University Press. Luhmann, N. (2016). System as difference. Organization, 13(1), 37–57. https:// doi.org/10.1177/1350508406059638 Mattheis, C. (2012). The system theory of Niklas Luhmann and the constitutionalization of world society. Goettingen Journal of International Law, 4(2), 625–647. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living (Vol. 42). Dordrecht, Netherlands: Springer Netherlands. Moeller, H.-G. (2006). Luhmann explained: From souls to systems. Chicago, IL/La Salle, IL: Illinois Open Court. Morozov, E. (2013). To save everything, click Here: The folly of technological solutionism. New York: Perseus Book Group. Paul, A.  T. (2016). Organizing Husserl. Journal of Classical Sociology, 1(3), 371–394. https://doi.org/10.1177/14687950122232594 Price, H. (2010). Time’s arrow and Archimedes’ point: New directions for the physics of time. Oxford, UK: Oxford University Press. Rovelli, C. (2017). Is Time’s arrow perspectival? In J.  D. Barrow, J.  Silk, K. Chamcham, & S. Saunders (Eds.), The philosophy of cosmology (pp. 285–296). Cambridge, UK: Cambridge University Press. Schwanitz, D. (1995). Systems theory according to Niklas Luhmann: Its environment and conceptual strategies. Cultural Critique, 30, 137–170. The Politics of Systems and Environments.

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Seidl, D. (2004). Luhmann’s theory of autopoietic social systems. Munich, Germany: Munich Business Research. van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and Communication, 1(1), 2–14. Varela, F. G., Maturana, H. R., & Uribe, R. (1974). Autopoiesis: The organization of living systems, its characterization and a model. Biosystems, 5(4), 187–196. https://doi.org/10.1016/0303-2647(74)90031-8 Wittgenstein, L. (1922). Tractatus Logico-Philosophicus (C.  Ogden, Trans.). London: Routledge and Kegan Paul.

CHAPTER 5

Finding Perspective

Try to visualise, for a moment, what your surroundings might look like if their interactive complexity were revealed to you in full. As I type this, I am sitting on an aeroplane somewhere above the city of Sydney. I know nothing about aeroplanes but it is one of those small commercial aircraft that buzz endless in the skies over Australia’s eastern states. In fact, so commonplace have aeroplanes become, it seems affected even to dwell on their remarkable complexities. Even if we are not regular fliers—and I am not, if I can avoid it—the functional and logical operations of the aeroplane are so familiar, so expected, that their failure, if and when it happens, causes an existential shock, like a juddering of the expected order. It’s almost as though something overwhelming and terrifying is revealed to us, the fragility of our expectations, which too often depend on us ignoring what we are not forced to see. When aeroplanes become commonplace they are reduced and made artificially simple by their repetitive action and our collective interaction with them, but they remain incredible complex systems. Sitting on this flight, I cannot begin to compute the countless mechanical processes and automated decision-making systems that somehow keep it airborne. They are marvels that work, not in abstract, but in a sky full of wind and pressure differentials and all the climatic complexity that the butterfly effect was coined to illustrate. Even isolated in the sky like this, hermetically sealed in a metal box with a handful of humans, the scope of interaction is incalculable. How many cells are there here, each of them bubbling and fizzing, the mitochondria © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_5

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whirring, the receptors firing? How many thoughts, each reflection a further differentiation of self, but many so subtle that even their owners miss their happening? How many computers hum and tick, balancing the wings and driving the jet engines? The numbers are unimaginable to me, the complexity so vast, how can we even begin to pick these things apart? What if we are worried about the alarming probability of flight? What if we want to know the specific interactions essential for keeping this plane in the air? Where should we begin to look? There is fundamental problem with complexity, which is that it is disorientating. It makes it very hard to know where to focus our attention, but our response to complexity should not be to ignore it, which it has often been. If we have done so, then partly this is because the theoretical challenge is considerable and so is the practical challenge. Our ‘focusing’ tools—our instruments for observation, measurement and analysis— impose a misrepresentative simplicity. We need to recognise that we cannot capture everything. Equally, we should recognise how easy it can be to get lost in complexity. There is a legitimate concern that the more we grasp for the complexity we don’t know, the weaker our grip becomes on the knowledge we already have—knowledge that we need to operate in a shared reality. If we reflect on the various questions we have considered so far, we see that time and again seemingly useful findings can be undermined by the obstructive claim that ‘things are simply more complicated than that’. It’s not especially helpful. How do we make this decision though? How do we know what is the right perspective from which to capture a system most completely and most relevantly given our reasons for enquiry? We do not yet have a set of principles for making such decisions, no calibration device and no equations to manage the calculations. We do not want to rely on guesswork or intuition though—system interactionism is little use if it cannot help us to focus our efforts in some way. Interactionism posits that reality is the sum of every interaction within an infinitely expansive field of interconnected systems, which sounds great until you start pulling on a particular thread and discover that there is no end to the unravelling. Wherever we begin our enquiry, whatever our initial systemic interest, we soon discover that the chains of potentially relevant interaction events run away towards the horizon. This is the same whether we are working with physical, social or symbolic systems—the temporalised nature of being means that interactive potential expands in at least four dimensions.

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In this chapter, my aim is to respond to these questions and to establish some principles for knowledge construction in the system-interaction field. I hope that these principles are logical responses to the challenges I have outlined so far—to complexity and an event-based ontology, and to the fundamental dynamic of always becoming. There are four principles that I will discuss in turn. The first is the necessity for filtration—that is, for a reflexive and self-aware framing of the problem that includes some interactions as relevant and excludes others. Second, I will discuss the implications of perspective, which states that the relative positioning of observer and observed shapes both the action and the outcome of observation. Third, I will ask what are the implications of all these things happening together in time? Fourth, and finally, I will consider the implications of these issues for an empirical methodology, and contend with relationism, which means that both observing and observed systems are differentiating independently as well as interactively.

Interaction Filtration System interactionism can play a role in advancing our understanding of complexity but it cannot reveal complexity to us in full—of course it can’t—because the implications of interactionism are vast. Everything is a system; no system is fully closed—interactions happen everywhere; influence is dispersed, temporary and phenomenological. Somehow, we need to find a productive method of engaging with this complexity without it overwhelming us—a balance between confidence in what we know and openness to what we don’t. Now, before I make the case for system interactionism as a framing tool, I must acknowledge that there are limitations to any knowledge constructed through a frame. I am about to suggest that system interactionism can work like a lens, a focussing device, which directs our attention within a wide field of interactive potential. If we get this initial framing right, then our observations can remain meaningful to us, applicable in a world of socially constructed action; we can make decisions about systems—about their operation and their influence—without despairing that we simply do not know enough. However, there are risks in framing knowledge. We may skew our observations or ignore relevant events because our frame relies on invalid assumptions. For these reasons Latour argued that ANT must be principally a descriptive method free of frames:

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A frame makes a picture look nicer, it may direct the gaze better, increase the value, but it doesn’t add anything to the picture. The frame, or the context, is precisely what makes no difference to the data, what is common knowledge about it. If I were you I would abstain from frameworks altogether. Just describe … we are in the business of descriptions. Everyone else is trading on clichés. Enquiries, polls, whatever—we go, we listen, we learn, we practice, we become competent, we change our views. Very simple really: it’s called field work. Good field work always produces a lot of descriptions. (Latour, 2004, pp. 66–67)

However, as I have argued, the temporalisation of observation is vital and utterly reliant on the contextualisation of study. Accepting that there is a temporal arc of becoming is a rejection of essentialism: histories matter and so does anticipation of the future—they matter for our interpretation of our observations in the present. What they should not do, however, is shape how we observe, the perspectives that we take and the methods that we use to record empirical evidence. Latour’s point is subtle: our contextual frames should not limit our capacity to describe the phenomenon we are observing. My argument is that limitations can happen in two ways: we can ignore complexity but we can also see too much complexity. In both scenarios, we limit our capacity to produce knowledge that we can use. We are seeking a productive perspective from which to observe systems in action, which will reveal sufficient complexity to advance our knowledge of these systems, while not confounding the observer with that complexity. I believe that such an approach is preferable to magnifying particulars, reducing complex systems towards singular features and isolated dynamics (to count media use in hours or to measure ideology through hashtags) and to the alternative, which is to frame the world in terms of unknowable details, and to speak in sweeping terms about meta-­ meanings and vague histories. In the same way that a camera lens can capture for us a fully realised subject, something clearly defined in a world of blurred shapes and smudged colours, system interactionism aims to distinguish significance from background noise. The key point to acknowledge in this metaphor is that the lens is only part of the framing process—the photographer must position the camera appropriately and direct its gaze. If he comes too close to the subject then the angles will defeat him; at too great a distance the subject blurs, the details become indistinct. In the same way, the system interactionist must frame the subject of her study, decide what is ‘in scope’

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and what is not, and make choices about the level of detail necessary to respond to the research problem. Consequently, the definition of system interactionism needs to be updated slightly following the discussion in the previous chapter. At the beginning of that chapter, I wrote, system interactionism is the temporal study of discreet interaction events between complex systems within an analytical reference frame. Systems selected themselves for analysis I suggested and I could well have been referring to system-specific self-­ differentiation, which is Niklas Luhmann’s explanation for systemic complexity, and an idea that I am borrowing to ground my analysis of the different ‘logics’ directing interactions within systems. A large part of the previous chapter was dedicated to a discussion of these logics of self-­ differentiation, and perhaps a slight confusion in that latter statement revealed itself. I conflated two ideas for convenience, first that systems exhibit some sort of internal cohesion—mechanics that bind them as things-in-the-world, at least for a time—and second that this internal cohesion presents these systems to us, complete and whole for our consideration. Obviously, this is not the case. We have rejected any idea of essential objectivism; we are focussed on events. We select the systems that we think we should analyse. We move the camera and we direct the lens. We can choose to observe a ‘complete’ system if we think that we can define its boundaries and that the complexity won’t overwhelm us. Alternatively, we can focus on a single component or a simple process within the system, and hope that what we learn will inform our understanding of the surrounding phenomena.1 A systemic reference frame is self-selecting to an extent but it is also and always artificial. We have to impose a reference frame in order to study the system because we need to limit the necessary observations; otherwise, our data collection would never end. An interactionist is forever making decisions about scope and these decisions are always apologetic. Yes, we say, of course we recognise the potential significance of that line of enquiry, but we need to build our knowledge from particulars—from specific contexts and specific perspectives—otherwise we will never finish before the system reconfigures itself. It’s a frustration, for sure, but it is also necessary precondition for how knowledge must work in an interactionist world. So where should we be directing our attention? How do we first recognise the interactions, processes and structures that are directly relevant to questions of influence in digital media systems? We cannot begin observation unless we first know the scope of the analytical frame. We cannot

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know the scope of the analytical frame unless we have a view on the logical operation of a system and its interactive potential. Such a view will be informed by historical knowledge, which in turn will be updated by new findings. As such, the reference frame is never fixed, never settled but always recalibrating, just like a lens adjusting to bring an image into focus. We are not wholly ignorant of these systems and often we have a strong sense of why they should interest us. Where does this sense come from? It comes from years of research in this area specifically and from decades and, indeed, centuries of work in related fields. Recall that all systems communicate in order to produce meaning, so the process of system identification must begin with a discussion about the ‘types’ of meaning that we are seeking to explain. Any discussion of meaning has the potential to be fraught, not least because the struggle to signify clearly extends to decisions about what should be classified as meaningful across the social and the material domains. Empirics cannot escape politics (Lacan, 1989[1965]; Nobus, 2002). Nevertheless, we must make a call about what is meaningful, given the context of our study, the assumptions that we are working with and the intended application of our investigation. Once we have established the meanings that we are seeking to explain, we can hypothesise the different systems involved in the production of those meanings. At this point, we need to be aware that there is a risk of circularity in our reasoning. Systems and meanings are mutually constitutive, discussion of one always demands reference to the other, and that constant referencing complicates questions of cause and effect. We have identified some meanings that we want to study; we explain those meanings as products of systemic differentiation, and then we seek the systems that are meant to have produced the meanings that interest us. How do we identify those systems? We identify them through the meanings that they produce, and so on it goes, an endlessly self-referential cycle of differentiation and production, in which we explain meanings and systems only ever through their association with each other. Part of the issue, of course, is the difficult conceptualisation of meaning—it’s relatively easy to identify meaning-making processes but far harder to pin down exactly what those processes produce. Meanings are imaginary in their very nature: ‘[S]ocieties … must communication and commune through the formation of overlapping or contiguous social imaginings—the sense of participating in “the group” through the mutual and interdependent construction of meaning’ (Lewis, 2008, p.  4). So

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meanings are the product of communication, which makes them the product of systemic interaction, but meanings are never fixed and instead are always being contested by other systems, by further communication. As such, we cannot begin any investigation assuming that there is a meaning we can observe unchallenged for the duration of our study. Consequently, we must understand the contexts in which we are operating and that demands a historical analysis of what we know already of the self-differentiation logics through which any candidate system came into being in the moment of study. That’s quite a confusing formulation, so let me try to state it more simply. In order to assess interactions between systems in the moment of study, we must first gather all the knowledge that we can about the components and the self-propagating processes that are assembled within those systems. We must locate our systems analysis within the historical record of systemic knowledge. There are echoes of Marx’s materialism here, of course, but primarily this principle is rooted in the tradition of assemblage theory. It is also common sense, I think. Before we can hope to understand productive interactions between two systems, we must first understand (to the best of our ability) how those two systems themselves came into being. We can say this in another way too: if we want to understand the meaningful-context of the interactions that we observe, we must first understand the historical differentiation that created that context. This means that system logics are fundamental to interactionist study. Chapters 6 and 7 attempt to establish the relevant context to frame an analysis of interaction between the digital and political systems. This is largely an effort to locate and explain the logics of internal differentiation that shape how the respective systems orientate themselves for interaction—to define the principles and processes through which they confront, interpret and respond to new information. This can be a difficult balancing act—historical readings of system logics must, at the same time, effectively filter complexity without reducing it too far, locking down paradigms and closing off possibilities for new knowledge. This is the first thing to say about knowledge production in a complex interactionist reality: it needs to be flexible, adaptable, ready to recognise and respond to new information. Chapters 6 and 7 are about making positional choices. Specifically, I aim to define what is relevant and what is not relevant and discuss how, in making that decision, I will shape unavoidably what I end up observing.

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Perspective Imagine for a moment that the act of research involves standing in a field. There is a fence around the field beyond which nothing is visible; the field is the bounding system, the only one available for observation. Exactly what the researcher observes in the field, however, is determined by two positional decisions. First, the research must choose a place to stand and, second, the research must choose a direction to look. Both decisions are clearly important and one must inform the other. For simplicity, let’s assume that our vision has an angle 90° and that the field is square. That would mean that a researcher could stand in any corner of the field and observe it in its entirety. However, standing in a corner means that the opposite corner of the field is as far away as it possible can be. That would not matter if there were no limits on the researcher’s ability to see, but ‘seeing’ doesn’t work that way. Even if we have 20:20 vision, there is a distance beyond which everything begins to blur. If the field is large, and we are standing in a corner, then large areas will be out of focus for us. As soon as we leave the corner, however, we have an additional directional decision to take. We may choose to position ourselves in the middle of the field, but our angled vision means that we will only ever know a quarter of the space. Whichever way we face, our view will be partial—a full 270° panorama will hide behind us and we will have no access to it. This simple analogy captures some of the most challenging complexities of producing knowledge in an event-based, interactive reality. Not only are objects more complex than they first seem, we find that we are implicated in that complexity: our limitations as observers add layers of mystery and uncertainty to everything that we observe. This realisation presented itself to scientists and philosophers in different ways, and many examples will be very familiar to readers. Special relativity concluded that objects are always in motion relative to each other. There is no such thing as a single fixed place to stand and view the world. After Einstein published his theories of relativity, there was general acceptance of his findings and then a period of argument among philosophers and physicists over what those findings might mean for the respective disciplines (Canales, 2015). I will consider the argument between Einstein and the philosopher Henri Bergson briefly in the section on time, but beyond these central protagonists, relativity prompted a period of intense uncertainty and insight. Some of this uncertainty drove advances in quantum mechanics, which produced some of the insights I have

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discussed already and, for a time, it ignited work in pragmatic and realist philosophy that questioned directly the separation between subject and object. There is not space here to give anything like a satisfactory account of the development of our thought around subject and object philosophy, of the controversies and the disagreements that are a central part of that story. All I can really do is state a position that I find more or less reasonable and hope that, while some may disagree, there is sufficient familiar precedent to ease the more acute concerns. Nothing that I’m about to argue is particularly controversial, though I accept that there is always a challenge in attempting to reconcile ideas from historically separate disciplines. Within their respective fields, all the points I am about to make are more or less established—indeed, most have been fully translated into general audience texts, which seem a fairly reliable indicator of acceptability. It is our limitations as observers, whether we are human subjects or mechanical measuring instruments, that cause us confusion about the subjective-­objective nature of reality. The great insight of George Herbert Mead and his fellow interactionists was that all observations, all measurements, including those taken by the most highly calibrated and ‘objective’ devices used in the natural sciences, are limited by perspective. Returning one last time to the camera metaphor, no matter how powerful the zoom, how high the resolution, how wide the viewing angle of a lens, there will be places that cannot be brought into focus. There is no solution to this problem as far as I am aware, but then why should there be? Indeed, to call it a problem is somewhat misleading and betrays a desire to ‘make’ reality into something more comfortable, more closely aligned to our historical knowledge, an unwillingness to accept reality as it is. For Mead, perspective was central to the realisation of the self but the concept was a logical and philosophical response to Einstein’s special relativity—it was a pragmatic engagement with material empirics, an acceptance of observed reality (Mead, 1932). Mead understood that there are ‘no absolutes, but rather a multitude or plurality of temporal-spatial relations’ (Nowotny, 1992, p.  436). He was making an argument about the unavoidable phenomenology at the heart of physics. It is simply not possible to measure a system without interacting with it in some way. That interaction is inherently limiting, and so that act itself is inevitably implicated in the realisation of the system under observation. Moreover, once we recognise that we occupy a single perspective on a system, we must acknowledge that there can be other

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perspectives also and that the system may look different from each of them. Reality is a perspectival construct: a system may look one way to us and quite different from another perspective. This leads to the quantum idea of super-positioning and to mind-bending theories of alternate realities, multiple dimension and a fully relativistic time-space. Percy Bridgman, who later won the Nobel Prize for physics, criticised Einstein’s wholly relativist view, arguing that measurement added validity to one perspective relative to others; this was ‘an obvious structure of experience’ he claimed (Bridgman, 1949, p. 354). Similarly Mead argued that interaction—or contact, specifically—lends something peculiar and particular to the experience of perspective: ‘[T]he thing is preëminently the physical thing of contact experience. … The distance experience is the promise of contact experience’ (Mead, 1932, p. 37). Perspectives involve contact and interaction between organisms and their environments. For example, a fish living in a certain pond can be thought of as inhabiting an ecosystem. The way in which it navigates the pond, finds food to eat, captures its food, etc., can be spoken of as the fish’s perspective on the pond, and it is objective, that is, its interactions are not a matter of the subjective perceptions of the fish. Its interactions in its environment shape and give form to its perspective, which is different from the snail’s perspective, although it lives in the same waters. (Aboulafia, 2008)

Perspective does not mean that there is no objective reality but it does mean that objective reality is always experienced locally, which creates the impression of subjectivity. As an observer moves around the interaction field, his perspective changes on the systems interacting within it. He is like the fish in the pond: it can expand its experience of the field by moving more widely, making ‘contact’ more often. However, no matter where the fish travels, nor where he directs his attention, it cannot share fully the snail’s perspective, and so at least two separate experiences of the pond will exist, and somehow we must account for both of them epistemologically. Reality is perspectival, which means that it is also probabilistic: if we look away then we cannot be certain that systems are not changing. All knowledge must be constructed in this way, the product of interactions between an observing system and an observed system-set. Quantum mechanics proceeds on a very similar principle: it recognises that material reality is effectively unknowable when we are unable to observe it. Only in the moment of observation does a physical particle become

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‘objective’—only then is it fixed into a position—and outside of that moment it exists in a weird state of uncertainty, in different superpositions, being at the same time both one state and another, in a subjective cloud of probable configurations. This was Heidegger’s great insight too—we come into ourselves through being in the world as Daesin. Heidegger also rejects the primacy of the subject-object division. We are part of our world and it is part of us; we come into being together. We are not subjects distinct from objects; rather we are all systems of complexity, interacting with each other: our reality, our being, is simply the perspective we hold on those interactions. This raises a question, of course: what is the world without us in it? For the phenomenologist, it is unknowable; for the quantum physicist, it is superpositions, everything and nothing all at once.

Time These discussions about subjects and objects, about perspective and Daesin, are also inevitably discussion about time. In Chap. 3, I argued that the study of systems was necessarily the study of their assemblage—their becoming—in time, which is why interactions between systems are our primary concern. If reality is event-based, then so is system interactionism. This is the nexus between phenomenology and physics, which Einstein argued about with Bergson, and which Whitehead, Mead and Bridgman attempted to explore through perspectivism. In this section of the chapter, I want to reflect on these arguments—on where they led and whether they were resolved—and to consider what it must mean for interactionism to be fundamentally a temporal study. The implications are profound, I think. Elsewhere, I have written that the study of interactions between systems is the study of time: ‘What is time if not the interactions through which those systems come into being?’ (Pond, 2020). Time is a problem for empiricists, as I pointed out in previous chapters. If we accept a quantum-phenomenological model of always becoming, then this has weird and confusing implications for reality and for how we must observe it. One of these implications is that perspective, as well as being spatially variable, must also be temporally variable. If we return to the researcher in the field, for a moment, we can see how this added dimension of variability exponentially complicates the construction of perspective. We have established that when the researcher moves around the field, her ability to observe the space changes according to the limitations of her vision. If she stands in a corner to maximise her

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field of view, then distant corners will be difficult to see; if she moves closer to them, then she obscures other areas. We can think about these dynamics in purely spatial terms. The field is a single plane through which the researcher can move, with two dimensions that we can plot against an x and y axis, with 0,0 being her starting point in the corner of the field. We can add a third dimension by giving her a stepladder, which will allow her to climb a little way above the grass and, perhaps, see a little further. This becomes a little harder to imagine and to represent diagrammatically, but only a little. We can add a third axis easily enough so that she can move up or down at any position in the field where she chooses to set down the ladder. What happens to our simple spatial diagram, though, if we add a temporal dimension? Things become considerably more complicated, not least because we still haven’t worked out how to draw satisfactory in four dimensions. If we were to draw the field, then most likely it would have to look like one of those perplexing diagrams of the block universe because this is effectively what we are now describing—a multi-dimensional landscape of temporal and spatial positions. The researcher’s movement is clearly much harder to describe in four dimensions. She can move forwards and backwards and across the field; she can step up above the two-­ dimensional plane and return down to it but now she must also decide when to make those movements. This is quite a significant additional decision because, of course, when she moves a new coordinate will influence what she sees when she arrives there. In fact, the decision is even more significant and complicated than that, because the moment of movement exerts influence in multiple ways. Most obviously, it can influence how the researcher is able to see the field from any position within it. If she starts standing in the corner of the field at time t and then returns to the same corner exactly 1 h later tn+1, then there is no guarantee that the field looks the same: the timing of the observation influences the interaction between the observed and the observing systems. There’s no simple way to visualise this happening because there are at least two different aspects in which the timing of an observation can exert influence. Let’s assume that the researchers make two observations at tn and tn+1 as described in the previous paragraph. Both observations take place in exactly the same spatial location (0,0,0) and involve the researcher doing exactly the same thing—looking out across the field, with the same capacity to see. We can accept quite easily, I think, that the field may have changed between those two observations. I set up this field as an empty

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space, which makes it a little harder to imagine the different ways it could change, but even seemingly empty spaces are full of activity: particles colliding with each other, electrons switching charge, fluctuating pressure differentials (not to mention a researcher dragging her stepladder all over the place). Once we start filling the field with details, though, we can imagine a multitude of dynamic possibilities. The field inevitably interacts with other systems. The wind blows and the sun rises, the grass could grow, or shift in the wind, because life buzzes and swarms, and no two moments are ever identical again. Even more confusingly, as the field changes, it may also change the researcher’s ability to observe. The sun rises and she can see further; it falls and the distant corners of the field recede into shadow. Perhaps the wind stirs pollen in the grass and her eyes water. As the grass grows so does the field; it becomes more voluminous, its matter is more densely packed and it may even push a little at its borders. Put simply, there is more to observe, more to record and more to miss. This is the crucial point: changes in the system may shape our ability to observe the system, so that no matter what we do, no matter how hard we work to standardise our measurements, the system itself can change how it presents to us. What if the field didn’t change? What if it were somehow in stasis, frozen as an essential object outside of time? In this artificial state, could the researcher repeat identical observations? The answer is still no because, to make any observations at all, the researcher cannot escape time. An observation requires action; it demands that the observing system is able to assemble and to make meaning, and that means that something within the system has to change, which binds it in time. If both the observing and the observed systems are in stasis, neither can change, and there can be no interaction: nothing happens. This is a crucial point, which follows inevitably from social system theory. An observing system must self-­differentiate in order to observe; otherwise it cannot recognise or process new information. Quite simply, without time, systems cannot make meaning in the world. However, as this discussion illustrates, the inclusion of time dramatically complicates an already complex situation. It was relatively easy to map perspective through three spatial dimensions, but now there are multiple temporal dimensions too, shifting around the spatial coordinates in a complex of relations. At the very least, there are three temporal arcs that we must somehow consider. The field differentiates temporally, the researcher differentiates temporally and the field and the researcher

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differentiate in relation to each other, also temporally. To understand what is happening and to consider fully how time complicates our observation of system interactions, we need to return quickly to how Einstein first posited the block universe, and question whether or not this is the best way to deal with the problem of time. The argument I mentioned earlier between Einstein and Bergson was broadly about the difference between subjective and objective experience, but it was specifically about time. General relativity posited a four-dimensional space-time that bent with gravity and predicted with remarkable exactitude the temporal behaviour of natural phenomena. This, Einstein argued, was the only time worth considering because it was the only time that could be measured—it had an objective existence. (Pond, 2020)

Bergson disagreed and continued to insist that time existed in places that could not be clocked, especially in our lived physical and psychic experiences. Their disagreement continued a centuries-old debate about the nature of time, which in western thought, at least, can be traced back to Heraclitus and Parmenides (everything changes vs. nothing changes), through Aristotle and Plato and Leibniz and Newton. One of the more remarkable things about arguments over time is how little that they have evolved over the years. I think that this is because time presents itself to us in two equally intuitive but experientially quite different ways. Aristotle and Plato defined these differences. Aristotle proposed what we now call the reductionist view of time and argued that time was simply our experience of events, meaning that it was subjective and relational. Plato defended an alternate view, which we now call absolute or substantive, in which time is universal and external from us, an empty container into which all the events in the world can be placed. The first view treats time subjectively, while the second treats it objectively. Einstein demonstrated that time could have an external, objective existence but could still be experienced differently by observers in motion relative to each other. He also demonstrated that it made no sense to insist on a universal present—a ‘now’ that was the same wherever one went, but in doing both these things, he folded time into movement within space (specifically, the movement of light) and for Bergson this missed something fundamental about the nature of time. Bergson preferred to differentiate between Einstein’s time, which he tended to call duration, and

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Time, which remained a lived, subjective experience better described by philosophers and poets than scientists. As far as Bergson was concerned, our inability to measure the private, sensory experience of becoming was insufficient reason to deny its existence. As the twentieth century dragged towards its close, the natural sciences drove the externalisation and the quantification of time to the point that physicists began to question whether there was any such thing as time at all. The continuing (and growing) dominance of industrial and free-­ market capitalism bent society to the rhythms of the clock; older social rhythms became increasingly unfamiliar. The study of lived or subjective time was pursued by a few social scientists and philosophers, mostly working within the continental tradition, and two interpretations of time became almost entirely alien to each other. It is quite common, now, to speak, not of different ideas about time, but rather about two separate times, one intimate and subjective, the other abstract but objective. As you have no doubt guessed, I am about to suggest that this separation of time into separate categories, one subjective and one objective, is reductive and needs to be addressed if we are going to engage fully with systemic assemblage. In my view, we already have the conceptual tools to reconcile that apparently separate experiences of time—they are the same ideas that allow us to reconcile the subjective and objective experiences more generally. Perspectivism explains how the system field can be experienced differently depending upon our position within it; the parallel logics of internal and external differentiation explain how different systems process the same phenomenon differently. Einstein’s scientific time does not preclude a personal or subjective experience, as long as we are thinking of systems rather than objects. Systems differentiate internally and externally and it is the relative experience of those two processes that creates the subjective view of objective time—that is, the local experience of the universal dynamic. Subjective and objective times are not different things—they are not even different interpretations of the same process. They are simply relative positions, different perspectives from which to observe time, which are selected from a vast array of possible positions in an infinite cloud of interactive events. Lived time or subjective time is our summary experience of all those interaction events that we can experience given our subject-­ bound perspective; objective time is a ‘measure’ of interaction events from a different (and hopefully standardised) perspective. When we quantify the universe, we divest our observation powers to set of mechanical tools,

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which we locate somewhere else in cloud and we assume that because we’re less central to the process, then what we observe must somehow be universal. The experience of time arises through the observation of interactions between systems, and these observations are bound by relativity principles. In the same way, this is how we experience systems. There is no special ‘subject’ position and it is certainly not something uniquely embedded in the human psyche. Our sense of being a subject comes from each of us having a particular perspective on a vast universe of interactive potential— none of us experience exactly the same set of interactions and from this our sense of subjectivity arises. This is not an easy argument to grasp and it can seem counter-intuitive, especially considering our long attachment to the subject position. I will return to it in more detail in later chapters because it has obvious implications for how we attempt to ‘measure’ systems, and those implications need to be fully explored. Imagine a man and a woman, both passengers, in separate cars both being driven at 60 kph for 60 s. Both stare out the window at the landscape speeding by and try to take in as much of it as they can. The man has a capacity to process a visual stimulus every 2  s—let’s pretend that this works a little like developing a polaroid photograph: the image is captured in half a second and then it takes another second and a half to materialise. So in that minute of driving, the man will have travelled a single kilometre, and will have developed 30 ‘mental photographs’ with which to remember his journey. The woman has twice his processing capacity. It takes her a single second to capture an image of the speeding landscape and to develop it. At the end of her drive, she has 60 photographs and a far more granular recollection of the journey she has just experienced. Hopefully we can already see that the woman’s greater capacity to process information about the journey gives her a different experience, even though both cars travelled the same distance at the same speed. We can quantify this still further. In that minute of driving, both cars travelled exactly 1000 metres. That means that that the cars covered 16.67 metres every second, and that every image (which took half a second to capture) must therefore represent roughly 8.33 metres of driving. Looking back on their memories, then, the man can enjoy a 250-metre journey and the woman double that—her greater processing capacity means that she has 500 metres of medium-speed driving to remember. Systems that self-differentiate at different speeds experience external differentiation differently. Crucially speed can refer both to motion within

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the world and to differentiation within the system, which means that the principles of relativity apply to the multiplicities of assemblage within systems as well. Most systems, remember, are structurally complex and differentiate according to multiple logical processes. These processes may run at different speeds or repeat with different rhythms. It’s the interplay between these rhythms that creates the rich and variable experience of internal time.

Relative Differentiation in the Interaction Field Like so much else in the systemic dynamic, perspectivism challenges us through complexity. Somehow, we have to deal with both the inherent instability of events and the fact that any interaction is—at the least—a shared construct between multiple temporal perspectives. That is, an interaction event involves at least two systems (an observer and an observed) and temporalities of both internal and external differentiation. Furthermore, each perspective has different levels of access to the interaction and different interpretative capabilities with which to decode the event. While an event fixes probabilities into certainties, this only lasts for the duration of the event and is still variable across the different perspectives. Personally, I find these multiple perspectives and transient probabilities highly poetic but incredibly hard to translate into prose. It is even more challenging to translate them into an empirical methodology. Imagine that we are interested in two systems, S1 and S2, and how they interact with each other. S1 is a simple system: it has four identical component sub-systems and each interacts in exactly the same way, creating a regular structure—essentially the components are the four corners of a square. S2 is simpler still: it has three components that also arrange themselves equally, creating a triangle structure. It might help to think of these systems as molecular structures or as independent friendship groups, but really it complicates matters unnecessarily to assign these hypothetical systems any sort of context or meaning. In the figure, below, I have drawn these two systems independently on the left-hand side of the slide and, on the right, illustrated two possible points of contact between them—that is, two possible configurations of S1 + S2 (Fig. 5.1). There are potentially an infinite number of configurations of S1,2. Even if we impose various restrictions on how the systems can interact, such as physical contact only (no intermediary exchange) or sub-system-to-subsystem contact only (vertices), there are multiple forms that the combined

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Fig. 5.1  Slide illustrating possible configurations between two simple systems

system can take. This raises the key question: when we observe S1,2 in a particular configuration (S1,2a), how do we know that this is the same configuration that persists once we look away? The short answer is that we don’t, which is why quantum mechanics deals with superpositions and why I have been describing the interaction field as probabilistic and diffuse. We can, however, attempt to describe the probabilities of different configurations based on the observations that we make, and perspectivism can help here. Initially, we require a couple of assumptions, but both these assumptions are theoretically amendable—they are priors in the Bayesian sense. In his reflection on Einstein’s special relativity, Percy Bridgman argued that measurement added validity to one perspective relative to all others; this was ‘an obvious structure of experience’ he claimed (Bridgman, 1949, p. 354). Our first assumption, then, is that this is indeed an obvious structure of experience and that therefore the configuration we observe is the most probable ‘true’ configuration of the system-set: S1,2a = S1,2. We can extend this assumption slightly if we make multiple observations, and say that the configuration we observe most often is the most probable true configuration. However, in the absence of repeat observations of multiple configurations of S1,2n, which might tell us otherwise, we have to make a second assumption, which is that the probabilities of alternate configurations are normally distributed around S1,2a. This dynamic is illustrated in figure below. The dotted line is the point at which the system is observed (S1,2a), the most likely true configuration of the system and the centre of a

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Fig. 5.2  An assumption of normal distribution of possible system configurations around event S1,2a

normally distributed bell curve. That curve captures all the other possible configurations of the system and the likelihood that we would observe one of these other configurations should we make a second observation. This is the simplest way to represent a probabilistic interaction field, and an acknowledgment that the system we observe may, at another time or from another perspective, look entirely different (Fig. 5.2). This is complicated enough but it captures barely half of perspectival complexity because, while S1,2 is happening in an indeterminate, probabilistic sense, perspective is also variable in several ways. As we have discussed, there are at least three different ways in which a system can change the perspective from which it observes an interaction. First, its coordinate position within the interactive field can move. The orientation of the square and the triangle will look different depending on whether it is viewed from the north or the south, from above or from below. Second, the direction of observation can change, as can the angle and the depth of field. An observer may be able to view the entire square-triangle assemblage, or it may be limited to a fractional view. Finally, the internal differentiation of the observer can limit its ability to observe. This is easily realised if we imagine the observer to be a human being, either long-­ sighted or short-sighted, unable to focus on large parts of the frame, but it is equally true if the observer is a highly calibrated measurement

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instrument. The large hadron collider enables scientists to capture the faintest traces of particle physics, but is a way of seeing that I cannot interpret. Similarly, I can use scales to observe the precise weight of an object, but the scales tell me nothing about its size or its shape. Different systems have different ways of seeing the world, and all ways are limited (Fig. 5.3). Thinking empirically, we have two domains of significant complexity moving in uncertain ways relative to each other; systems are only probably the way that we observe them to be, and our observations are limited, perhaps in ways that we may not realise. If we try to make repeat observations to firm up our probabilistic system knowledge, we must somehow also attempt to standardise our observation perspective, though this may not be possible and we may struggle to know if we have achieved it. No wonder that Percy Bridgman was intent on establishing the primacy of the observation position, but like the operationalists subsequently discovered, measurement alone will never reveal a phenomenon completely. So what should we do? How can we respond methodologically to this complexity so that we are at least producing useful knowledge? First, we are going to have to use probability theory, at least in our observation of interacting systems, so that we can express our confidence in the system configurations that we observe. Second, we are going to have to find a way to interpret events from the perspective of all the

Fig. 5.3  An illustration that different observer perspectives may influence the observation of an interaction event

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relevant systems that includes those in the interaction-set and it includes us, as observers and researchers. If possible, we will have to find a way to limit or to slow the differentiation of the observer-perspective so that we can be somewhat confident that we are not primarily responsible for any changes that we observe. Finally, we are going to have to temporalise our research, possibly with more mathematics, so that we can monitor the internal and external differentiations of observer and observed systems relative to each other. Readers may be grateful to know that I don’t intend to engage with the mathematics in this book because I want to establish the underlying concepts first. The solution that I suggest here, and work through in the coming chapters, remains highly reductive. I don’t yet know how best to model the inherent uncertainty of system-observations in a way that represents the diffuse possibilities of the interaction field. That’s a task for another book. For now, I am going to rely on two rather large assumptions to sidestep the maths. I will assume that the observed system configuration is indeed a true representation of the ‘true’ (i.e. most probable) system configuration, at time t, where t equals a clock-time measurement corresponding to the moment of observation.

SNo = SNt

Arguably this is not an assumption that I should be making given the amount of effort I have invested in describing the considerable complexity that it ignores. I would reiterate the point from the earlier paragraph, though, that this assumption is just a prior, to be updated as more knowledge is produced through better methods. We begin by assuming that the configuration that we observe is the true configuration; if a second observation reveals a different configuration then our assumption changes, and the process continues through more and more observations as our assumptions grow narrower and our confidence in the recorded configuration grows. What does this look like in practice. Imagine that we are interested in the internal system dynamics of Twitter, particular the practice of retweeting. We want to know what variable or logic has the greatest influence on how many times a given tweet is retweeted. We suspect that the most influential factor will be the number of followers that the tweeting account has (popularity), and so we ask two users, one with 1000 followers and

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one with 100 followers, to tweet the same text at the same time from the same location. After a few hours, we observe that the tweet from the first account has received 632 retweets while the second tweet has 23 retweets. What do we assume? As yet, we have nothing to say about the relationship between followers and retweets. All that SNo  =  SNt asserts is that 632 is indeed the number of retweets that the first account managed in the period we were studying. We assume that the system we observed is the same as the system that would have assembled if we were not observing it. I am also assuming that the relative differentiation of separate systems can be assessed against an external, standardised clock time. This is not quite the same as saying that time itself is the Newtonian t, but I am assuming that it is possible to construct a system that repeats regularly and reliably for the duration of study and that this regular rhythm creates a reference against which variable systems can be compared. In practice, this means that every interaction that we observe can be assigned a timestamp using standardised clock time, and that the timestamp retains comparative value both within and between systems. I like to think of this clocked time as being like a diver’s downline, a tether running from the surface down with the diver into the underwater workplace. I believe that cavers and potholers do something similar. They anchor a rope on the surface and carry it with them into the darkness, it maps their route and, if they need it to be, it becomes a lifeline to lead them back into daylight.

A Temporal, Perspectival, Empirical Methodology Before I conclude this chapter and move on to a more direct engagement with digital-political interaction, I want to describe briefly how the epistemological issues I have been discussing can translate into methodological principles. Each of these principles will be developed in far more detail in coming chapters, so a brief statement of intent will suffice here. I suspect that I have only made the complexity of interacting systems seems even more daunting by raising the issues of framing, perspective and time. This methodological discussion is not going to dissolve that complexity but, then, it is not meant to. The complexity problem cannot be solved by wishing it away; somehow we have to find a way to embrace complexity, to develop our knowledge away from reductive objectivism and towards a more intuitive grasp of perspective and uncertainty. I approach interactionist methodology in a couple of different ways, both of which are necessary to fully realise a science of interactionist

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empirics. First, I try to conceptualise how the different complexities should influence our pursuit of knowledge in the interaction field. This is largely an attempt to reason logically with complexity, a series of ‘if A, then B’ type statements. For instance, what if we are interested in the implications of super-positioning at the macro-system scale? If we accept that systems exist in a cloud of possible configurations until the moment we observe them, what does this suggest about the configuration that we observe? Well, if we also assume that the configuration cloud is normally distributed, then the configuration that we observe is most likely the system configuration that occurs most often. Following this initial attempt to respond logically to a complex problem, I attempt to rationalise or quantify what the logic must mean for the empirical data collection. Normally, given that complexity almost always means that knowledge must be expressed in terms of relatively certainty, this involves probability calculations and a Bayesian-approach to confidence, but it may also mean that I conclude that there is insufficient information to justify any sort of belief. Recognising and appreciating gaps in our ability to know systems is also an essential part of the response to complexity. This is not a book about interactionist methodology and so I don’t intend to address this second-order response in detail. I should note, also, that attempting to quantify uncertainty with probability calculations is not essential and that it may be perfectly sensible to respond to these same problems qualitatively. Indeed, in the coming chapters, I largely rely on critical reading to interpret the empirical statements that system interactionism delivers. Not all systems interact and produce meaning that is easily represented numerically. Indeed, there’s a fairly compelling argument that Silicon Valley’s obsession with quantification (and, of course, the sale of advertising on the back of those numbers) has contributed enormously to the hollowing-out and the polarisation of our discourse. For now, then, it will suffice to establish three principles of an interactionist methodology—three empirical techniques to recognise and respond logically to complexity within the interaction field. First is the problem of framing or filtration, of knowing where to focus our attention when interactions are everywhere. The problem, at least, is easy enough to describe: we need to filter complexity so that we are not overwhelmed by it. Inevitably, that means that we need some knowledge of the systems that interest us—and how those systems work—to direct our attention towards interactions that are relevant.

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Framing is an assessment of topical or contextual relevance. It answers the question: given what we know already, and given what we want to know, where should we look for interaction events that are probably relevant? Probability is a Bayesian prior—a best guess given the current state of our knowledge, and theoretically quantifiable, though we don’t need to assign any numbers to make this conceptual argument. Topical and contextual relevance are functions of meaning-making. Relevance is decided in response to the question: what interaction events contribute to the production of meanings that are relevant to our research? Framing, then, is largely a question of what is meaningful in the interaction field, and meaningfulness is considered from (at least) three different perspectives: the two interacting systems and the independent observer. The production of meaning becomes the logical arbiter of contextual boundaries. Empirically, we can begin to analyse systems that we have negotiated and pre-selected as meaningful in a given context. Once we have established those logical boundaries—and that logic is clearly a function of specific meaning-making processes—we move on to the second principle, which asks us to engage with the complexities of perspective. Recall that perspective is a product of both where we position ourselves for observation and how we conduct the process of data collection. We must not forget that we are just another system engaged in interaction. How we record information about interactions matters, but so does our interpretation of that information. We make meaning through different logical processes, relying on different theories, different conceptual frameworks, different ways of knowing, to make sense of the interactive complexity all around us. If our logical operations differ between observations then we will never make comparative sense of what we see. Choosing an ‘appropriate’ perspective is therefore exceedingly challenging. One option, perhaps, is to engage fully with the complexity and try to capture events from multiple perspectives independently but, then, this raises a question: how many is enough? Can we hope to estimate the total number of possible perspectives on a single event? If we could argue that ‘total perspective’ is solely the sum of user experiences (and it is clearly not, there are other material and non-material perspectives to consider), then potentially we could limit our description in this way. How, though, do we work out which user experiences are relevant? Political publics are often national and sometimes international, and it may not be easy to ‘ring-fence’ a discourse community in any meaningful way. We face the possibility that our ‘multiple perspective’ count quickly runs into the

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thousands, if not millions. Describing any event from so many angles is practically impossible but it also creates a fundamental epistemological problem: how can multiple accounts be synthesised into a single representative account? The way to work out what actually happened on the steps of the Lincoln Memorial is surely not to interview everyone who saw a video of the confrontation online. Alternatively, we could prioritise a single perspective above all others. This was Percy Bridgman’s approach; being critical of Einstein’s pervasive relativism, he argued that measurement added validity to one perspective above all others. This approach, though, leads to an almost impossible metaphysical question: what is the ‘correct’ perspective from which to validate reality? It also ignores a great deal of interactionist theory (and several sections of this book), which clearly argues against the idea that measurement validates one—and only one—instantiation of reality. Other measurements are always available. Perhaps we could apply Bridgman’s logic slightly different. Rather than use it to validate a single perspective, we could commit to a critical evaluation of all the ways in which that single perspective is limited. Clearly, this is itself limiting, but it is surely better to recognise the complexity limiting our empirical work. The third option is to attempt a description of a limited but representative sample of perspectives and then try to fold those perspectival accounts into a single empirical description of systemic interaction. This ‘halfway’ approach has certain advantages but there are obviously complexities too. Assuming that we have identified relevant perspectives and made observations of events from each, we will then need to combine those observations into a summary description of assemblage. Any empirical combination will need to be weighted and adjusted to reflect the interactive complexity of assemblage, but at present, we have no method for this. More fundamentally, there must be a rationale for choosing between perspectives. The word ‘representative’ is covering enormous problematic complexity. What is being represented exactly, and how is representation evaluated? These questions are particularly difficult because perspective depends on that initial contextual framing, so they demand answers that are also context specific. At the same time, some sort of standardisation is also clearly necessary,2 because otherwise we will never produce comparative empirical work. In the final chapter of the book, working with a specific set of questions in a specific context, I attempt to address these complexities. Finally, the third principle of interactionist empirics relates to the temporal nature of systemic study. Interactionism is the study of events in

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time, which means that any influence or logic that we infer from those events is specific to a particular arc of temporal becoming. This can be pretty confusing but, essentially, it means that all events are temporally bound to specific ‘histories’ of assemblage—we cannot abstract our knowledge from this temporal context. In effect, time—or, more specifically, the inescapable temporal nature of assemblage—adds another dimension of perspectival complexity to the business of event observation. Both where we observe and when we observe matter to what we observe. Fortunately, the empirical solution to the temporal challenge is more straightforward than the perspectival challenge, because it is one dimensional. We aim to observe event-driven change within system configurations. In other words, interactionist empirics begins with an event (or an event-set) and then tracks event-responses over time. As researchers, we only ask comparative questions of thematically aligned events (e.g. øt = øt+/−1). As before, this is context-specific work, and it is more easily described once the technological and political frames have been established.

Notes 1. System interactionism is a reminder that an image is made up of pixels and that those pixels give the image its colour, contours and its depth. It can be a useful reminder because this isn’t how we have learned to think about the world nor about images. We tend to regard pixelated images as imperfect, blurry approximations of the reality they are meant to capture; we think of them as inferior to the smooth, continuous lines we can draw with modern vector graphics tools. This is why we cram more pixels into every squared centimetre of screen space, why our jpeg files grow ever larger and why our televisions now stream ultra-high definition feeds. We expect reality to be crystal clear. This is the wrong way around. Quantum mechanics has revealed for us that reality is granular. The raster image is the more accurate representation. At the quantum scale, nothing is continuous, everything falls apart: objects, forces, even time are all, at some level, discreet entities. 2. This is largely the reason for empirical science. Science proceeds on the assumption that as long as we can control experimental conditions and repeat standardised observations, then objective reality will be revealed to us. Systemic complexity undermines this assumption, of course, partly because it rejects a reality made from objective essences and partly because interactive complexity is so great in most systems that the promise of experimental ‘control’ is largely illusory.

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References Aboulafia, M. (2008). George Herbert Mead. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy (Vol. Spring 2020 ed.). Stanford, CA: Metaphysics Research Lab, Center for the Study of Language and Information, Stanford University. Bridgman, P. (1949). Einstein’s theories and the operational point of view. In P.  A. Schilpp (Ed.), Albert Einstein: Philosopher-scientist: The library of living philosophers (pp. 333–354). La Salle, IL: Open Court. Canales, J. (2015). The physicist and the philosopher: Einstein, Bergson, and the debate that changed our understanding of time. Princeton, NJ/Oxford, UK: Princeton University Press. Lacan, J. (1989[1965]). Science and truth. Newsletter of the Freudian Field, 3(1/2), 4–29. Latour, B. (2004). On using ANT for studying information systems: A (somewhat) Socratic dialogue. In C.  Avgerou, C.  Ciborra, & F.  Land (Eds.), The social study of information and communication study. Oxford, UK: Oxford University Press. Lewis, J. (2008). Cultural studies – The basics. London: Sage. Mead, G.  H. (1932). The philosophy of the present (A.  E. Murphy Ed. Franklin Classics (2018) ed.). London: The Open Court Company. Nobus, D. (2002). A matter of cause: Reflections on Lacan’s ‘science and truth’. In J. Glynos & Y. Stavrakakis (Eds.), Lacan and science. London: Karnac Books. Nowotny, H. (1992). Time and social theory: Towards a social theory of time. Time & Society, 1(3), 421–454. Pond, P. (2020). An event-based model for studying network time empirically in digital media systems. New Media & Society, 1461444820911711. https:// doi.org/10.1177/1461444820911711

CHAPTER 6

Autobots Assemble

This chapter is dedicated to the self-differentiation of technology—it reviews what is already known about the assembling logics of digital media systems. At least, that is the aim, to the extent that it is possible to isolate any system from its wider interactionist environment for analysis. It can be tempting to assume that a technology only interacts externally once autopoiesis is ‘complete’, once the technology is mature or fully formed, as though it had been incubating in vitro until that moment. The process is much more complex than this, of course. According to the framework we have been developing, autopoietic processes shape logics of system assemblage, which in turn shape the potential for interaction with other systems in the environment. Throughout this process, a system communicates with its environment—which is the central point of the constructivist position. Technology is shaped by its being in the world, just as all systems are shaped in this way, including humans. There are obviously reasons why it is so tempting to describe a technology (at least initially) as something distinct and complete in the world, an object with a linear and explanatory history. The complexity of systemic interactivity can be overwhelming—in order to parse and process this complexity, it is sometimes necessary to resort to abstracted object-­ orientated models. Additionally, the temporary isolation of a system may help us identify particularly influential internal system interactions (which, we may then reason, are likely to affect external-facing interactions in some way). We should not assume that all interactions are born © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_6

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equal—different events exert different influences over the system. There are specific laws governing the distribution of influence within a system but also because of chance, historical differentiation or whatever other reasons, some interactions have greater absolute or contextual significance for an observer. There is a difference between a raindrop and a meteor falling to earth and that difference—that influence—is the scale and the number of subsequent events that the initial collision initiates. Furthermore, digital media systems are already mature technologies— the only way to study their historical differentiation is through whatever accounts of those events exist. Historically, we have produced and categorised knowledge around objects, mostly, and within discreet objective taxonomies. (Most books are written about things and attempt to describe those things fully within their pages.) If we want to describe the modern internet, it’s clearly necessary to draw on a range of sources from across disciplines, from mechanical and electrical engineering, computer science, economics and political economy, media studies, cultural theory, empirical social science and many more. The skill lies in how we identify and frame those sources, what we extract from them and in how we reconcile that knowledge into a coherent narrative of technological differentiation.

A Working Theory of Technology If our aim is to describe and explain the differentiating logics of a technology, then presumably we should have in mind some sense of what that technology is—a definition that we can agree upon, which can focus our study. If we are going to seek that definition in academic history, then first we may have to conceptualise that history. We need this meta-­conversation because the interactionist approach complicates definitions in much the same way that it complicates objects—a definition changes depending on the perspective that one takes and on the duration the definition must last. A digital media system can be defined according to its internal differentiation logics but it can also be defined by the way that it presents and acts in the world. Those parameters are frequently changing. So, what is a media technology? How does it function? The discussion of these questions by media theorists predates the internet by many years. More than half a century ago, Marshall McLuhan famously argued that ‘the medium is the message’—an analysis that located technological forms right at the centre of the human experience (Coupland, 2010). For McLuhan:

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the ‘message’ of any medium or technology is the change of scale or pace or pattern that it introduces into human affairs. The railway did not introduce movement or transportation or wheel or road into human society, but it accelerated and enlarged the scale of previous human functions, creating totally new kinds of cities and new kinds of work and leisure. (McLuhan, 1964, p. 8)

McLuhan’s medium-centric theorising is sometimes characterised as technological determinism, or the assumption that social action is best explained by the technologies that make that action possible. A huge part of the definition of a media technology is the effect that it has in the world. Determinism elevates the role of technology at the expense of the individual subject, and it associates function closely with effect. It is an appealing and sometimes compelling idea, but was critiqued by Raymond Williams (1975), who preferred to emphasise the social practices and cultural forces embodied in technology and its application. For Williams, it’s simply not sensible to define a technology through its functional effects, because there are social (and subjective) influences throughout the development and application of new technologies. Social reality is a co-­ construction: situated in social and cultural norms and structures, partly human endeavour, partly subjective and partly technological. The other point to make about determinism is that the technology precedes the social context. Determinism is concerned with the influence that a technology has once it interacts with social systems and tends to worry less about how that technology came into being itself. This can be a curious analytical stance because technologies clearly arise within societies; they are never developed in isolation. As we will soon see, the internet was originally a Cold War technology—that context is embedded in some of the design and engineering decisions that continue to shape differentiation logics today. As a result, when we seek to retell the differentiation histories of different technological systems, it is never sufficient to frame those histories as though they are purely (or even predominantly) technological histories. As Janet Abbate (2000) argues in her history of the early period of internet invention, there are social and cultural factors that influenced design and development decisions, and those decisions cannot be understood without that contextualising information. Or, as Thomas Hughes wrote in his account of the electrification of western society, any ‘effort to explain the change involves consideration of many fields of human activity, including the technical, the scientific, the economic, the

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political, and the organizational. This is because power systems are cultural artefacts’ (Hughes, 1983, p. 2). The relationship between humans and their technologies was explored by Heidegger, who identified in modern human experience a bias towards interpreting objects as standing reserve—as raw materials that can be subjected to technical action (Heidegger, 1954). The effect of this dependency is that world is ‘revealed’ to us in ways that subject it to technological interpretation: the forest is fire wood, rivers are to be dammed and the rock beneath our feet is meant for mining. Andrew Feenberg (2005, 2017) extends this insight, arguing that the ‘rules’ that govern this revealing are shaped by class and power and the institutions dedicated to those hegemonies: Technology is a two-sided phenomenon: on the one hand the operator, on the other the object. Where both operator and object are human beings, technical action is an exercise of power. Where, further, society is organized around technology, technological power is the principle form of power in the society. It is realized through designs which narrow the range of interests and concerns that can be represented by the normal functioning of the technology and the institutions which depend on it. This narrowing distorts the structure of experience and causes human suffering and damage to the natural environment. (Feenberg, 2005, p. 49)

As such, the description and analysis of technology within society must proceed with a dual intent, which Feenberg formalised in ‘instrumentalization theory’. It must capture and assess the functional features or ‘affordances’ of a technology (as in the answer given above) but it must also consider the contextualising information (ethical, cultural and aesthetic) which ‘qualifies the original functionalization by orienting it toward a new world involving those same objects and subjects’ (p. 51). I intend to pursue a constructivist approach and will attempt to locate key moments of technological differentiation within a wider account of social and cultural interaction. But where to begin this account? It can be argued that the internet in its current form is a product of centuries of socially situated technological innovation and development, of system-­ specific decision making and environmental influence. Can we fully grasp the significance of networked computing if we don’t first fully understand the non-networked computer or appreciate how the original Turing machine automated complex calculation functions? How can we explain

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the drive to develop a calculation machine into a communication machine without appreciating the economic, financial, political and military logics driving an age of global expansion? The short answer is that we can’t. The invention and the differentiation of modern digital technologies are inexplicable without the long arc of human history—that’s not quite the same as saying that they are impossible, but the odds definitely look prohibitive. To a significant extent human history is enacted through different technologies: technologies of warfare, enlightenment, rebellion and expansion (Misa, 2011). All we can really hope to do is look at periods of transition, moments of development and replacement, to compare what there was to what became and to ask what logics were shaped in those moments. How and why were decisions taken, what was settled by those decisions and what was destabilised, what logical structures were made and how stable were (or are) those structures? The study of systems is the temporalised study of becoming; we are focussed on events, not on essences. As such, our task becomes one of identifying and describing the historical moments of systemic change that produced the logics that shape the assemblage and meaning-making of the digital systems we want to study today. As established, we are not trying to describe every interaction but those that were particularly significant or influential—we are looking for meteors rather than rain drops. There is likely to be some disagreement, of course, concerning how much influence should be assigned to specific differentiation events, but there should largely be agreement around the current logics that we are seeking to describe and to explain.

The Search for a Logical Definition The problem of definitions has troubled internet studies since its conception, and it’s a problem that is compounded by the field’s proximity to the buzzword factories of Silicon Valley and entrepreneurial capitalism (Morozov, 2013a).1 For many years, we used the term ‘social media’ as though it meant something singular and obvious despite unpacking it quite differently to suit different interests.2 The truth is that we still don’t have an authoritative definition of social media—one that is always useful and never problematic. Understandably, the definition changes as the technologies that it seeks to describe change, and this fluidity should caution us against assigning features or properties to the social media category and then incorporating them into the definition.

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In Chap. 2 I introduce the idea of social media logic and the case made by the Dutch academics José van Dijck and Thomas Poell (2013) that different technologies can be aggregated under the term social media because they share similar logics of self-differentiation. It’s an idea that I plan to return to, not necessarily because I agree with the conclusion that all social media is characterised by these shared ‘technicities’ but because I think that the concept captures some of the essential categories of interaction that we should be observing. van Dijck and Poell argued that social media is different in fundamental ways from mass media because it obeys different ‘norms, strategies, mechanisms and economies’ (p. 2), which in turn produce functionally different outcomes. While mass media is characterised by narrative news and the pretence of detached or neutral rationality, institutionalisation and corporate power, social media responds to different rules written in a wider ‘network culture’. Inferring from these conditions, we contend that social media logic refers to the processes, principles, and practices through which these platforms process information, news, and communication, and more generally, how they channel social traffic. Like mass media, social media have the ability to transport their logic outside of the platforms that generate them, while their distinctive technological, discursive, economic, and organizational strategies tend to remain implicit or appear ‘natural’. (p. 5)

Logic refers to the ‘processes, principles and practices’ through which media platforms organise traffic. There is something intrinsically systems-­ centric in the framing of these technologies as broader social ‘things’ are able to act and exert influence in the world. That framing uses logics that they define through a dialectic between, on the one hand, internalised processes of differentiation and, on the other, external or environmental flows of information to be processed. Departing from a Luhmann-style systems analysis, however, the authors’ suggest that these logics are written externally and culturally. How that writing takes place, exactly, is not clear but we may assume that there is a level of constructive exchange between culture, environment and social media ‘platforms’ through which repetitive events are translated into logical structures.3 In effect, van Dijck and Poell are characterising social media technologies by the logical principles through which they ‘operate’, where operation effectively translates into meaning production. Their approach, then, offers a mechanism for defining social media as a category that moves

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beyond assumed shared features or superficial similarities (which are forever changing) and instead prioritises modes of systemic action and interaction. We cannot rely completely on their work to define social media systems because, as they accept, the logics they describe are not meant ‘to provide an exhaustive analytical model of social media logic’ but rather to ‘identify a few of its main contrivances’ (p. 5). We can, however, use their insights to develop a framework for the description and classification of media systems that share a selection of interactive logics. Those logics will organise a category of computer technologies and be interactive with other systems in ways that we wish to study. Van Dijck and Poell begin with four separate ‘elements’ that they believe differentiate social media from mass media systems: programmability, popularity, connectivity and datafication. I will spend a little time on each of these elements, explaining the rationale behind them and considering their importance for a logical definition of social media. First, though, it’s worth reminding ourselves how a logic is meant to arise in our self-differentiating model of systemic becoming. How do we explain the existence of these logics? Reality is event based and all events involve an interaction between different systems. Some events occur at random and seem not to contribute to an established order of increasing stability and complexity, but others repeat in predictable ways, and these repetitive sequences of events become integral to systemic order, forming first processes and then structures. The driving force behind this ordering of events is autopoiesis, which was Luhmann’s term for a logic of self-reflective identification, which begins with original and essential difference and builds on this difference through communication, reinforcing the distinction between self and environment. Once we begin to talk about social media or, more broadly, about digital media systems, then we are effectively saying that some of those logical structures are shaped by interactions with specific digital technological components. At least, this is how I define a digital system—it is a complex assemblage that requires digital technology to produce meaning through communication. Shortly, I will explain in detail what makes a technology digital, and what differentiates a digital system from other technologies, specifically analogue systems. For now, it will suffice to note that digital technologies tend always to integrate computing logic with distributed networked architectures. We can say that the logical elements that van Dijck and Poell identify are the product of processes and structures that engage digital technology

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in the production of meaning through communication. First, programmability, which is ‘the ability of a social media platform to trigger and steer users’ creative or communicative contributions, while users, through their interaction with these coded environments, may in turn influence the flow of communication and information activated by such a platform’ (p. 5). Programmability recognises that within digital media systems, interactions between sub-systems are rarely random but instead are directed by automated decision making processes, assemblages of code, working on extensive data structures in ways that reflect the intentions of the programmer and the environments in which that program was working (Beer, 2009). Programmability is not meant to be deterministic—users can resist encoded instructions and circumnavigate suggested practices; users can also innovate within those processes and shape emergent decision making logics, because pre-programmed loops will respond to user inputs and behaviours. Nor is programmability only characteristic of social media logic. Broadcast media is programmable too, channels run schedules and pre-plan media events (Katz & Liebes, 2007) to maximise audience attention, but the extent to which the internal environment is programmed, and the degree of automation in this programming, is considered qualitatively different in digital systems. Social media programmability would be less significant, perhaps, if it were not for the collection, processing and storage of massive amounts of data, both on interactions that happen within the digital system and on the of attributes and behaviours of independent system-units. Computing power makes this scale of data collection possible, as well as the relatively low cost of server hardware and data storage (Boyd & Crawford, 2011). van Dijck and Poell use the term ‘datafication’ to refer to this massive data collection effort, which is used to quantify systemic information that could previously only be described in qualitative terms. If I were to ask you how much you liked one friend compared to another, you may have various ways of considering that comparison, but you are likely to decide based on a combination of emotions and perceived interactions. Facebook can quantify the answer for you, reporting exactly how frequently you have interacted with each friend, on what terms and with what outcome or effect. Does this quantifiable matrix capture exactly your feelings about your respective friends? I would argue no, because of complexity and the reasons I have described previously, and because of inescapable representational limitations, but then there are limitations that apply to your qualitative response too. Feelings change, we are not always perfect

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judges of feelings and we often struggle to find the words we need to fully capture our feelings. Digital systems are able to self-reference in qualitatively new ways because they capture so much data and can communicate with that data through automated pre-programmed processes which in turn establish precedents for the collection of more data. Perhaps most significantly, they can do this incredibly quickly. Scholars have long noted that one of the defining characteristics of digital systems is the speed at which they are able to communicate (Castells, 2010; Gehl, 2011; Hassan, 2009). ‘Real time’ action within these systems can happen because the technologies they incorporate can support programmable action on data quicker than the human systemic components can respond (Berry, 2011; Weltevrede, Helmond, & Gerlitz, 2014). This hyper fast, data driven activity often appears to happen without direct human interaction—that is, programs direct other programs to collect, process and act on data, and then that action feeds back into future programmable logics. It is unsurprising then that van Dijck and Poell conclude that: the principle of datafication has profound implications for the shaping of social traffic. … We should try to understand these complex dynamics not just as they unfold within the boundaries of social media platforms proper, but in their confrontations with different logics dominating other institutional contexts (p. 11)

One of the consequences of the programmable data-environment, it seems, is that it rewards ‘popularity’, which is frequently quantified within the system. The tendency of digital systems to reward popularity with more popularity should be familiar to anyone with even a passing knowledge of social networking platforms like Instagram, Facebook and Twitter. In these systems, popular accounts and popular topics are promoted through programming and ‘pushed’ back at users through various recommendation processes. Once again, this self-reinforcing popularity is not something unique to digital systems: mass media models and society more broadly have always relied on existing popularity to project future attention-­grabbing potential. These different mechanisms interact with each other in ways that mean popularity in one system is likely to drive popularity in another. The result is a stratified systemic environment that looks quite different from the early, egalitarian view of the web.

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Popularity within one part of the system also drives popularity in another. By far the strongest predictor of the influence of a single Twitter account is the number of followers that the account has. Popular accounts produce tweets that shape discourse under hashtags and this influence reinforces the popularity of accounts (Pond & Lewis, 2019). As a result, social media logic is partly defined by the influence that users exert over discourse and, by extension, over the wider meaning-making potential of the digital system. This is an important point to make because popularity is typically shaped through politics and prejudices, the greedy drivers of the free markets and social norms and expectations that the ‘cyber-­ utopians’ hoped that the internet would destroy (Jeff Lewis & Best, 2003; Morozov, 2013b; Rheingold, 2000). In effect: social media logic complements mass media logic and enhances its dominant norms and tactics, just adding an extra dimension. … What makes this element of social media logic different from mass media logic, though, is its ability to measure popularity at the same time and by the same means as it tries to influence or manipulate these rankings. (van Dijck & Poell, 2013, p. 7)

The fourth and final element of social media logic is ‘connectivity’, which is meant to refer both to a demand for inter-connection within individual systems—that is, between sub-systems—and to a culture of connection between different commercial systems like Facebook and YouTube. Of course, system interactionism assumes the potential for connection between all systems, but the connectivity element of social media logic is meant to emphasise the hyper-connectedness of these techno-­ systems. It ‘refers to the socio-technical affordance of networked platforms to connect content to user activities and advertisers’ (p. 8). There are architectural, cultural and economic dimensions to this connectivity, perfectly captured in Facebook’s relentless insistence that life is better shared. There’s an element of social aspiration to this claim—in many respects, life is more joyful when shared with other friendly humans—but there is also the cold reality of Facebook’s commercial model, which monetises the ‘sharing’ of life by selling those interactions to advertisers. At the same time, the ‘openness’ of that sharing culture is liberating, challenging (certainly in respect to privacy) and ultimately, perhaps, quite atomising. ‘Connectivity introduces a bipolar element into the logic of social media: a strategic tactic that effectively enables human connectedness while pushing automated connectivity’ (p. 8).

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This connective logic of social media has been aligned with a movement within the social sciences that has promoted ‘the network’ as the dominant structure in modern society (e.g. Castells, 2004; Wellman et al., 2003). According to this argument, modern technology has promulgated an age of networked individualism, in which everything including social and economic status, political action, consumer preferences and creative endeavour is coordinated through transient digital connections. I tend to agree with critics (e.g. Erickson, 2012) that the network has been oversold both as an analytical tool and as an explanatory metaphor. In particular, the tendency to capture all connections as data has resulted in us seeing networks everywhere, imagining them to be determining structures, and too often losing sight of their representational weakness. I accept that there are networks but not all networks are meaningful—or, in the language of systems, not all networks contribute to the productive communication of systems. An emphasis on connection is already implicit in system interactionism, because ultimately that is what a connection is—it is an event in which two entities come into contact with each other. All systems are ‘connective’ in this sense and so the connectivity principle must be interpreted as meaning that social media logic is somehow more connective than other system differentiation processes. Is this interpretation reasonable? Digital systems might enable forms of connection that were not possible in analogue media systems. An obvious example here might be the remote working or teleconferencing tools that permit colleagues to meet in ‘real time’ regardless of physical proximity. In this sense, we are dealing less with ‘connections’ as objects, but once again with the coordination of interactions in time and space. It is the detachment of connection from temporal and physical immediacy that is significant—that is, the ‘systematic perturbation in the sequential order’ of connection (Castells, 2010, p. xli).

Logical Precedents Programmability, datafication, popularity and connectivity are elements that together shape a meaning-making logic that is typical of social media systems. As such, they play a vital role in our conceptualisation of these systems, helping us to recognise them, and to explain why these systems operate in the ways that they do. These four terms are analytical descriptions of differentiation processes, so they also lay the groundwork that we

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require to identify social media systems interacting in the world and they establish parameters for how we should observe these systems. One could make a case that there should be more (or fewer) logics in the list. There’s a fairly compelling argument, for instance, for adding ‘commercialisation’ or ‘monetisation’ because an intent to profit is deeply embedded within the other logics. Equally, replicability is a distinguishing logic of the digital system compared to the analogue: digital media can be copied materially and symbolical in ways that physical media cannot, and this replicability shapes communication practices. Equally, we could debate whether all four logics deserve equal billing in any definitional account of social media. Arguably neither popularity nor connectivity is as foundational as programmability, which shapes differentiation within both logics. Programmability operates on data, though: a system that does not produce data is static because there is no new information for it to respond to. The point is that a list of four logics doesn’t resolve a definition, but nor will a six item list. The important point here is that system definitions should be malleable because systems change. The benefit of using logics to describe social media is that logics are dynamic, fluid and evolutional; they are assemblages themselves that may last for a long time or may exert influence only briefly. Recall that a logic is a repeating pattern of events—there is nothing essential or objectively definitional: they are a description of a type of differentiation in time. Their explanatory power comes from the fact that we may observe them changing and perhaps locate those changes in a historical or developmental record. That developmental record sets precedents, it may describe a path of becoming, it may also suggest a taxonomy of logical classification, which may itself hold some explanatory power. For instance, I think all four social media logics can sit in a logical taxonomy beneath two overarching ‘digital logics’, precedents shaped by the history of digital invention: the first is automation and the second is networking. If we can write a history of technological development, then we may understand a little better the systemic and environmental conditions in which those logics formed—and that might reveal a good deal about why social media has the logics that it does currently. In my view, two periods of invention are particularly important in shaping the digital tools that we have today. I intend to begin my history of digital technology with an initial leap forward in automation—the invention of the Turing machine—and develop it through innovation in networking, first the invention of the Advanced Research Projects Agency Network (ARPANET) and later the development of software to further exploit packet switching technology. First, however, my choice of those

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two overarching logics requires some justification. Why automation and networking and why not something else? Automation and networking are suggested by ‘reverse-engineering’ the social media logics. All four items in that list are products of either automation or networking or a combination of both. Programmability refers to the ability of social media technologies to manipulate data through code, which is a tool of abstraction and automation. Code automates information manipulation through calculation, comparison and logical differentiation, and that requires information in specific data types: integers, strings, Booleans and so on. The information restrictions of automated calculation drive the datafication of all types of physical and social events—rather than use nuanced emotional descriptions to distinguish between relationships, we must use quantifiable measures, like the number of interactions, the duration of those interactions and the sentiment of words exchanged during those interactions. Popularity is an easy measure of success in a programmable environment. Abstract and socialised values can be fixed as ‘metrics’ by counting likes or follower numbers. Those metrics are operable, so they are datafied and looped back into functional manipulation, so that outputs become inputs, the logics further assert themselves and the system self-propels its differentiation. Programs run the same loops unless new information is received, and so digital systems require new resources to grow, to adapt and to develop. Popularity is already a key logic of differentiation and increasing popularity requires new resources and those resources demand greater connectivity—a network expansion. Network connectivity is low cost and widely distributable, and so the four logics work symbiotically to capture resources within the network and to maximise the extraction of data from those resources. Automation demands networking for growth; networking is supercharged by automation; automation maximises the extraction of resources from the networked environment. Automation and networking are distinct logics, meaning that they are not reducible into each other in the same way that the four social media logics have just been treated. What makes them distinct from each other? There is a technological answer to this question, which is that automation arises within a different domain of practice and invention from networking—one is about calculation and the other is about communication (in the narrower sender-receiver sense of the term). According to this response, the internet is a product of two distinct systemic lineages, which when assembled together produce new logical structures, combinations derived from separate antecedents.

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Technology In Vivo The argument just presented is moderately convincing, perhaps, without being wholly satisfactory. It still treats technological systems as though they were isolated, only ever self-referential. It doesn’t fully recognise the social and cultural influences that shape those technologically distinct domains or the similar and fairly obvious problem that technologies—and therefore logics also—are largely produced by humans. We cannot rely on a purely technological explanation of our logical precents because such an answer cannot ever tell us why change happens when it happens. A full explanation requires a contextualisation of that differentiation, a description of the conditions in the 1950s and 1960s that facilitated the marriage of computational and communicational technologies. For a start, it was a marriage that happened in a specific place at a time of heightened social, philosophical and military tensions. Those tensions drove productivity but they also demanded specific types of productive response. In other words, there were wider systemic and environmental logics involved in the differentiation and development of the technological systems. The logics of automation and networking and, by descent, the social media logics cannot be fully understood without those historical precedents. The most relevant history relates to the mid-twentieth-century period when much of the development in electrical and information engineering happened in the US and was funded by the Pentagon and, in particular, the Department of Defence’s Advanced Research Projects Agency (ARPA). ‘No force in the twentieth century had a greater influence in defining and shaping technology than the military’ (Misa, 2011, p. 190). Alan Turing first imagined the modern computer in 1936 and famously applied his ideas in support of the Allied codebreaking effort in World War Two (Hodges, 2014). Turing conceived a programmable digital machine, capable of carrying out operations or calculations on a data store according to instructions reducible to binary numerals (Turing, 1950). Automation allowed the consistent repetition of processes at impossible speeds compared to anything attainable ‘by hand’. Turing’s universal machine and, later, his Automatic Computing Engine established logical precedents that were advanced during the 1950s, mostly in the US and mostly through military sponsorship (Flamm, 1988). The history of that intense and innovative period has been told in many excellent books and does not need retelling here. Suffice to say that,

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by the early 1960s, computing technology had been developed to a point that it is clearly recognisable as such, even in modern terms. Processors were less powerful, data storage and handing technologies were material and cumbersome, but computers were handling programmable repetitive processes just as Turing had imagined they would, transforming daunting (or even impossible) human activities into functional inevitabilities. What were those logical precedents? Automation did not begin in the twentieth century with Alan Turing. Charles Babbage has demonstrated the Difference Engine a full century before and established the foundation principles of automated reason (Bullock, 2008). Automated processes are repetitive but they are also mutable. They respond to programming decisions made by human coders, and if they change during operation they do so according to those initial logical precedents. They operate on data, and the demands of various mathematical logics mean that data must exist in specific forms. The functional and typological requirements for data assert enormous influence over the wider system. Data constructs a ‘way of seeing’ the world that inevitably mutates into a ‘way of being’. As Boyd and Crawford (2011) argued with remarkable foresight, automating research through data collection-processing changes our very definition of knowledge, creating a quantified but abstracted epistemological frame, which is largely divorced from philosophy and alternative intuitive or embodied ways of knowing. Automation inscribes digital systems with a self-reflective logic that is profoundly different from systems that prioritise alternative (analogue) ways of knowing themselves4 (Hassan, 2018). Before we turn our attention to modern automated systems, though, we should reflect on the way that networking logics have fundamentally changed the isolated calculation machine that Alan Turing devised. While codebreaking techniques were being formalised into computing technologies, other wartime innovations were transitioning into longer term projects, with both military and commercial implications. Radar, especially, but also by extension semiconductor and transistor technologies, advanced hugely in the decade after the war ended, as US military strategy prioritised investment in them. In the development of transistor technology, ‘we see how the tension between military and commercial imperatives shaped the emergence of a technology that today is fundamental to our society’ (Misa, 2011, p. 213). The names of Raytheon, Bell Laboratories and RAND Corporation occupy imperious, enigmatic and vaguely ominous positions in the technology history of that period. These three are perhaps the most famous of

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several companies that benefited from enormous military investment to further develop wartime technology with a limited agreement for commercial exploitation. Bell, for instance, worked on transistor technology with the aim of transforming its commercial telephone network, but Cold War priorities diverted money and attention away from civic projects and on to missile warning systems and infrastructure protection strategies. The second of these demands drove the development of early networking technologies. It was ARPA that facilitated the development of packet switching technology which made it possible to transmit information between digital computing machines in data streams consisting of blocks or packets. In the simplest possible sense, the internet is a series of connections between computers made possible by packet switching technology (Leiner et al., 2009). The ability to connect computers communicatively was conceived and described by different scientists in several papers during the early 1960s (Kleinrock, 1964; Leiner et al., 2009). A series of collaborations between these researchers, their laboratories and the US government led to the engineering, in 1968, of ARPANET—the first functioning packet switching network of computer-computer connections. ARPANET promised a distributed and interconnected data store, which would be resilient to any one of its individual connections failing—a potentially valuable attribute in the face of a nuclear strike or some other Cold War catastrophe (Castells, 2010). ‘Survivable’ communication technologies were key components of the defence strategy because they promised that command and control would continue even under sustained military assault. The invention of packet switching, its deployment in ARPANET and the subsequent period of experimentation within this networked environment by both military and civilian users shaped the communication logics that continue to influence modern digital systems. Packet switching encoded messages into ‘digital’ form, which meant that anything from recorded speech to computer data could be represented as ‘packets’ of binary numbers. Any computer user will be familiar with the recurring numbers of computing, data transfer and storage, which ultimately derive from the packaging of binary numbers into units of 8 called bits. Multiple bits were packaged as blocks (1024 bits), encoded with header information to direct switching nodes in the newly invented distributed networks to forward blocks to their intended destination. Packet switching, which was conceived independently by researchers in both the US and the UK, enabled rapid data transmission through

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distributed networks that transcended geographical distances (for a full history see Abbate, 2000). It also married computing to these networks, inscribing in modern digital systems the twinned super-logics of automation and communication. It is important to re-emphasise that this marriage was neither organic nor inevitable but the result of decisions taken by social actors embedded in wider cultural and political systems. Although the initial motivation for connecting computers in strategic locations across the American content was to protect data from attack, researchers, their universities and eventually other civilian users were granted access to ARPANET. In the current climate, it may seem astonishing that the US government was so generous and open with its strategic technologies (though perhaps the Snowden revelations give us pause to reflect on this generosity), but it was. A variety of users groups were asked to experiment with ARPANET during the 1970s and they contributed enormously as its astonishing communicative potential began to be realised. In part, this may have been because initially ‘ARPANET was underutilized, and ARPA had little reason to discourage users or activities that might make the network more popular’ (p. 85).

Confusion and Logical Webs Many features of what is today called the internet are, more accurately, applications that run on the internet and its Transmission Control Protocol/ Internet Protocol (TCP/IP) architecture. As ARPANET evolved into the internet, via many different, smaller scale networks (CSNET (Computer Science Network), USENET), the communication applications that the network supported became increasingly integral to the concept of the internet itself, so that in 1985, the Federal Networking Council (FNC)—a US federal body set up to manage the sharing and coordination of infrastructure costs and development—issued the following definition: The Federal Networking Council (FNC) agrees that the following language reflects our definition of the term “Internet”. “Internet” refers to the global information system that—(i) is logically linked together by a globally unique address space based on the Internet Protocol (IP) or its subsequent ­extensions/follow-ons; (ii) is able to support communications using the Transmission Control Protocol/Internet Protocol (TCP/IP) suite or its subsequent extensions/follow-ons, and/or other IP- compatible protocols; and (iii) provides, uses or makes accessible, either publicly or privately, high level services layered on the communications and related infrastructure described herein. (Leiner et al., 2009, p. 30)

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On the whole, the applications that shape digital media systems are children of the World Wide Web (WWW), which itself is an application that runs on the internet: ‘The Internet is like a network of electronic roads criss-crossing the planet—the much-hyped information superhighway. The Web is just one of many services using that network’ (Cailliau & Gillies, 2000, p. 1). The Web is actually a suite of integrated technologies, famously invented by Tim Berners-Lee, a software engineer at the European Organization for Nuclear Research (CERN), in 1989 (Berners-Lee & Fischetti, 2000; Connolly, 2000). The first is Hypertext Mark-up Language (HTML), in which web pages are written, and which is read and interpreted by browser applications in order to display content. The Uniform Resource Identifier (URI) is an address system necessary for locating individual resources stored somewhere in the Web-running domain of the Internet. Finally, the Hypertext Transfer Protocol (HTTP) is employed to request and retrieve Web-specific resources. It is simply another communication protocol, just specific to the Web, but it is highly influential in that it permits the linking of resources and the creation of the ‘web’ effect that is so crucial to our understanding of digital information flows and connectedness (BernersLee, Cailliau, Loutonen, Frystyk Nielsen, & Secret, 1994/2003). The development of HTML, URI and HTTP made possible, for the first time, the internet applications that served content in a way that was accessible to general computer users (Abbate, 2000). Crucial to this development was CERN’s decision to make these technologies public and royalty-­free in 1993. In the early nineties, web documents were very different from their modern equivalents: Berners-Lee’s invention permitted the identification and retrieval of information via the internet, but it did little to make that information as engaging, appealing or as influential as it is now. That transition was vital for transforming the Web from a communication and information tool into the social and culture ecosystem it is today (Cailliau & Gillies, 2000; Hindeman, 2010). Facebook and Twitter are Web technologies that run on the internet. They are alike and they are different because although they make use of the same underlying network, they are designed, engineered and used differently: they enable different services and different communicative experiences. Sticking with precise technical definitions permits a clear distinction between these technologies, based on programming choices (code), data storage and User Interface (UI) design. This is the world of software (Fuller, 2008b; Manovich, 2001), which is distinct from the

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computational and network architecture discussed so far, but ‘at least as important to the modern electronic calculator as its “hardware” of tubes, transistors, wires, tapes and the like’ (Tukey, 1958, p. 2). Such is the significance of software in shaping the functional differentiation of digital systems; it is now subject of its own academic discipline: software studies (Fuller, 2008a). The benefit of a software studies approach is that it seeks to combine a deep understanding of software with the social analysis of practices through which software is realised. The following extended quote from Bernhard Rieder is particularly instructive in terms of defining software technology in terms of its social significance: ultimately, if our business is not with the matter of how mechanical computation is possible in the first place, but with software as an object in-the-­ world. … The elegant concept of computation then quickly begins to bloat up with many different things: real computers, not just abstract Turing machines; real software, lodged in tight networks of other software, all written for a purpose; knowledge, ideas, skills, tools, methodology, habits, and values that permeate practices embedded in layers of social organization, cultural configurations, economic rationales, and political struggles. (Rieder, 2012)

It’s a reasonable criticism, I think, to say that in our analysis of the internet to date, we are yet to resolve satisfactorily the relationship between the idealised or elegant logics of automation and networking and the bloated business of software in the world. This is the tension at the heart of the determinism versus constructivism debate: to what extent do the ‘pure’ engineered logics of computation and networking permeate the software that runs on that architecture and the (far messier) interaction with human users of that software in the world? Another way of framing this question is to ask: how do the super-logics of automation and networking become in the world through software? In many respects, these are questions of mass interaction, at least when compared to the relatively small and specialised systems that shaped the interactive environment through which ARPANET was realised. Early internet technologies were developed by mathematicians, engineers and programmers working in small communities and largely influenced by the same social and political concerns. The second 30 years of internet history, which is largely a history of internet software, is exponentially more

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expansive. Internet technologies may be thought of as ‘layered’ (Bruns & Moe, 2014): a bedrock of networked, computational architecture grounds a subsoil of navigational software (the web) above which there is a topsoil of different applications, where the majority of humans operate (Pond, 2016). The idea behind this analogy is that the bedrock shapes the topology of the subsoil which in turn conditions the topsoil: features permeate the layers, rising towards the surface in different places in different strengths, with variable effects. It is better to think of this dynamic in terms of systems and time. Such a framing is both more precise and—I think—more accurate, and it is better equipped to support the observation and the analysis of these processes. The systemic description is better than a spatial metaphor because it captures the essential event-based ontology of the digital system. It recognises that systems change and develop and can explain why permeation is variable. The problem with spatial metaphors is that they parenthesise time or ignore it completely. The logical implications for networked systems are difficult. Far better, in my view, to recognise that ARPANET shapes modern digital systems through a shared history of differentiation. It becomes simpler to recognise and to account for change, both within and between systems, and it becomes easier to analyse influence. Given the spatial metaphor, how do we explain why the influence of the networked architecture permeates variably? If the bedrock is the same everywhere, and the subsoil is the same, why does the topsoil take such weird forms? System interactionism is much better at accounting for the messiness of variable differentiation. Why are individual digital systems seemingly so similar to and so different from each other? It is because they are always under tension, always becoming, made similar by the shared logics of historical differentiation and made different by the divergent experiences of new interactions.5 In this framing, a digital system is the product of three interacting spheres of assembling logic: there is the networked computer, there is hypertext and there are software applications. Interactions between the networked computer and hypertext shape the logics of automation and networking; interactions between hypertext and individual software applications shape the logics of digital media systems.

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Affordances and Affects The historical differentiation of networked software technologies involves human systems at every stage. With luck and sufficient application, a historical account of these interactions will identify events that were especially influential in shaping logics of differentiation—logics that continue to condition the interactive potential of the modern technology. So we have a broadly constructivist account of technology, in which technological systems are realised through a cyclical process of situated interactive becoming. As a result, and if we have done our work properly, we will have some reasonable expectations for how a technology will behave in the world—we may even be able to predict the type of interactions that are likely to take place (with a specific external system) and—if we are particularly well informed—the influence that those interactions are likely to have. In other words, we will be literate in the ways of the technological system. Literacy is a vital idea in a communicative philosophy of systemic differentiation. We cannot inherit knowledge of objects—we can only observe systems interacting and interpret the meanings that those interactions produce. Our ability to interpret interaction events must surely depend upon our knowledge of context, as well as our knowledge of system logics and our own capacity as observers. Our literacy (i.e. our contextualised knowledge and our ability to apply it) shapes both our observation of systems but also our own interaction with them. This realisation is crucial for rejecting a deterministic view of technology (and systems more widely). The computer on which I am typing is a different technology in my grandmother’s hands and different again in the hands of a skilled engineer. It is ill-suited to the task of hammering nails, but if I had a nail that desperately needed hammering and no other tool available, I daresay that I could get the job done. It may seem fairly obvious to say so, but we cannot account fully for the differentiation of a technology if we cannot account for how that technology interacts with its users—we need to be able to describe both how it is made and how it is used. ‘Use’ may not be the right word here. It implies the type of asymmetry that we previously criticised in a deterministic view of technology, only leaning this time in the opposite direction, raising difficult questions about agency, power and value assignment. The system interactionist does not assume that contact between systems will proceed in one direction or another; it seeks discreet interactions, the influence of

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which will need to be parsed and interpreted. In other words, humans don’t control technology any more than technology controls humans, not by default at least. One may control the other for a time, but control arises because the sum of interactions between the systems tends towards control in one direction or the other. The effect is summative and transitory. How, then, should we frame the ‘use’ of a technology by human beings for social action? What is a better way to describe the process of interaction between systems if there is no overarching logic of directional or functional control? The suggested solution is to get particular, to focus on the assemblage of interactive parts and to describe the distribution of influence in more granular detail, but there are a couple of issues with this approach. The first is that it is endlessly recursive, explanation is always deferred, there is always a more granular scale, a level of more particular detail, ‘beneath’ the surface of the phenomenon we are trying to explain. This is a general problem with posing a granular social science—the quantum only applies at the quantum scale—and we are only ever (pre)tending in that direction. The second issue is that particularity is rather hard to translate into productive knowledge—the type of knowledge through which we can reach consensus and make decisions. Particularity produces the type of knowledge that gets eyes rolling and wears patience thin: academic knowledge in other words, the type that revels in things always being a bit more complicated than first they seem. Determinism was appealing because it suggested a uniform, easily interpretable role for technology in the world, and because it married nicely with a techno-capitalist ideology that was becoming dominant in the final decades of the twentieth century. A cogent critique of constructivism (and, indeed, of post-structuralism in general) is that it offers little in the way of practical solutions to the recognised problems of the world. Imagine that we want to know whether or not we should regulate a particular technology and, ideally, in what ways we should regulate it. That decision is impossible if we cannot say what the influence of the technology is, or how that influence works or what our regulation will do. We need some sort of framework for collating all the observations we make of all the interactions we observe, otherwise we cannot make collective decisions about the technology. We don’t necessarily need to know with certainty, we can recognise that there is variability, but we want to make decisions on the balance of probabilities and to agree that those decisions are justified. In other words, we need a framework to help us interpret the differentiation of technological systems in the world and in the hands of human beings.

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According to system interactionism, assemblage begins with interaction events that mark difference; those events are communicative, they involve (at least) two discreet sub-systems and they contribute to the greater production of meaning within the system. When events involving the same systems happen repetitively, or in particular logical sequences, they are called processes and stable processes can be integrated into structures. These ideas and this ordering define autopoiesis in Luhmann’s social system theory. The different meaning-making processes and structures within a system, in sum, are what we have been calling logics and they shape continuing differentiation. When we are dealing with how a system behaves in the world—rather than how it differentiates internally—we are interested in how those logics shape the potential for interactions with other systems. We can describe logics after observing internal differentiation but, at some point, we must redirect our attention towards how those logics orientate a system towards its environment. Fortunately, scholars are already using frameworks to help them interpret the role of technology in the world, and at least one of these maps nicely on to this systems-generated position of logical interactive potential. Affordance theory was originally proposed by James Gibson, a cognitive psychologist. For Gibson, writing about the relationship between animals and the environment, an affordance was something offered or suggested by the environment to the animal: it ‘implies the complementarity of the animal and the environment’ (Gibson, 1979, p. 56). Gibson’s argument was that behaviours or actions become possible only through interaction between man and object (objects being the ‘furniture of the earth’). When observing an object, man sees not some innate, intrinsic or absolute qualities but instead affordances—what seems promising in terms of object-­ human interaction. For technology theorists trying to avoid determinism, this is a useful distinction. Gibson’s theory was adopted by media theorists when some of the more optimistic and deterministic predictions for the internet began to unravel in the late noughties. There was still a general acceptance of form and function—it was still possible to map social networks, for instance— but clearly while the internet may suggest opportunities for action, it could not assert them. Affordance theory offered an appealing language for describing the complexity of this relationship: it ‘defines a technology in terms of the uses, interactions and possibilities that the technology affords to its users’ (Fray, Pond, & Peterson, 2017, p. 4). In other words, it describes a suggestive or ‘shaping’ influence and not a determining one

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(e.g. Joinson & Piwek, 2013). In discussion of affordance theory, there has been a growing emphasis on the enabling potential of interaction between a skilled user and technology to make possible a full range of potential outcomes. As Pond et al. (2019, p. 78) explain: An affordance is neither a feature nor an outcome of the technology; it depends upon the perception, intention, skill and imagination of the user to act in different ways, or to effect outcomes in different contexts. It is thus useful to categorize affordances according to their interactive potential.

Interactivity is central to the affordance concept. A technology affords certain possibilities to its users by making available one set of possible interactions while denying others. Consequently, affordance theory frames technology squarely as a communicative construct (Evans, Pearce, Vitak, & Treem, 2017). This can be challenging for an object-oriented ontology (what is an affordance if its existence is only ever suggestive (Oliver, 2005)?) but, for a system theorist, this is wholly compatible with an interactive, perspectival reality. In fact, across the humanities and the psychological and behavioural sciences there is tacit recognition that technology is more fully realised in the hands of humans whom are able to ‘read’ it successfully. In education, training is targeted at digital literacies because interpretative skills are considered more powerful or more enabling for users (e.g. Miles, 2007). It can be useful to think of an affordance as being something like an interactive spectrum, with the technological object at one end, and the user at the other. In between are various possible interactive outcomes, shaped to a greater or lesser degree by the functional properties of the technology and by the abilities (or limitations) of the user. A highly skilled user has more ways in which to manipulate a technology; a highly functional tool (e.g. a hammer) or, indeed, a highly designed tool will direct the user towards a far narrower range of actions. This, at least, is the general principle. In phenomenological terms, we can say that the technology and the user are only ever fully realised through each other. That realisation is not predetermined; rather it is constructed through interaction, in the moments when two systems of logical influence meet each other. As such, we can say that the affordances of a technology are shaped by the interaction between two sets of system logic, and we can see how the idea complements the processes of interaction and differentiation that we have been describing in this chapter. Automation and networking present

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architectural logics to the designers of software, who respond to these logics as affordances and both expand and further differentiate the system, building software applications and media tools with their own logics, to which end users must then respond. In this way, systems everywhere present themselves to the world, suggesting possibilities for interaction without ever having full or determining control over how that interaction proceeds. Sometimes the course will be set by the ‘stronger’ system, the one able to exert more influence in the moment, and sometimes chance will intervene. This is the sense in which reality is diffuse and probabilistic until the moment that systems collide with each other and set a course of becoming. The key insight here is that process is always competitive. When two systems meet, both are engaged in a semiotic negotiation (Lewis, 2005).

From Social Media Logics to Logical Media Systems The different theoretical questions being discussed in this book arise from an original set of concerns about truth, disagreement and uncertainty in the media generally and in digital media particularly. The media landscape in which those concerns arise is vast and includes a huge range of ‘social media’ technologies: Facebook/Instagram, Twitter, YouTube, Reddit, Forums, Blogs, WeChat, Weibo, and of course there are many more. To a large extent, all these systems do have social properties, as do the websites and wider digital ecosystems of the legacy media companies—the New York Times, Washington Post, The Guardian, The New Yorker and others. Social logics are deeply inscribed in the modern internet—if anything that tendency has increased in the years since van Dijck and Poell first attempted to characterise a ‘social’ logic as something new and distinct. Consequently, the logical shaping of affordances happens in largely similar ways across many of the digital systems that contribute to the global mediasphere. While I am not suggesting that it makes sense to treat all these different technologies in the same way, I do think that there is some sense in beginning with the four social media logics and adapting as appropriate. Certainly programmability, datafication and connectivity are inherent logics across most if not all networked software systems and, frequently, there are logics of popularity too—marketplaces incorporate review or voting mechanisms, news sites allow comments and voting, recommendation networks operate across systems and so on. What’s more, automation continues to rely on ‘popularity’ as something representative of importance or quality.

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So many of the digital media systems that contribute to the political sphere exhibit social media logics, but they are also complex systems, each with its own tangled assemblage of differentiating logics that must be studied individually and assessed fully. It is challenging to be specific and precise, both in proposing technical definitions and in applying those definitions in more general discussion. The definition of social media logic may be partial, there be characteristics missing from the list and there may be disagreement over some of those characteristics. As I have just established, logics arise through internal differentiation and external differentiation; they may look different depending on the systems involved. Even if it were possible to agree on a set of logics that fully defined social media, what about the other internet technologies, those that are not ‘social’? In fact, given the interconnected character of the modern web and—a growing issue—the monopolistic dominance of a few digital mega-­corporations, does it really make sense to separate a special category of ‘social’ media from the more expansive sense of ‘digital media systems’? Modern Facebook is a global mediasphere, a massively influential political actor, hugely careless, often destructive, a sweeping marketplace and now, apparently, a producer of currency and an increasingly independent economy, so its scope defies easy definition. It is also, in part at least, a social media site operating according to those four logics and those logics permeate the other operations of Facebook, because those operations are enabled by the same automation and networking technologies. I am not attempting, here, to try to define Facebook nor to count its many logical operations. What I’m trying to do is make a case that ‘social media’ as a category is defunct. In fact, any attempt to sort digital systems into discreet categories is unhelpful, partly because of the many complexity issues discussed in Chap. 3 and partly because automation and networking are foundational to all of the systems. Rejecting these tired categories should not inhibit our work to try to understand the influence of specific software assemblages, nor techno-­ corporations like Facebook. There are still plenty of ways to frame digital systems for study. In fact, I find it relatively astonishing that we ever thought it productive to apply so many essentialising labels to a global web of digital systems. Clearly this is a dynamic and emergent system-­ environment, clearly it is highly interconnected and clearly the pace of change will challenge any categorical type that we try to impose. To my mind, it seems equally clear that there is a better way to intuit these systems and it is to focus on the dynamics of logical differentiation by which

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they assemble. I use the word ‘dynamics’ here advisedly. I suspect that we will always want to categorise logics in the same way that we do the systems that they produce, but this should be avoided. There is no point in substituting one set of eternal system-objects for another and pretending that the problem is fixed. Logics will change as the patterns of interaction between systems change. The point of focussing on logics is that this change then becomes central to our analysis of how these systems work. Automation and networking shape logics that influence how the software interacts with external systems, making it possible to deconstruct how meanings are negotiated and perpetuated through discourse. The study of this happening seeks moments of systemic ‘collision’, in which one set of priorities struggled for influence over another. More broadly, history is a record of systemic struggle, of moments of collision and an effort to divine meaning in the outcomes. This chapter set out to explore what is recorded about the historical differentiation of modern digital technologies, beginning with the development of the Turing machine and acknowledging the considerable military involvement in the invention of networking technologies. This sort of historical accounting allows us to identify moments of systemic collision and to reflect upon their influence. It tells us something about the technology that exists now. The modern internet is a product of countless technological, sociological and political collisions, each of which was a communicative event, an act in a wider struggle to signify, which continues between systems, each trying to differentiate itself and to become meaningful.

Notes 1. I don’t think that it’s an exaggeration to say that our collective struggles to understand the complexities of these technologies—especially the unfamiliar dialects of computer and information science—have, at times, left us over-­ reliant on the vocabularies and the presumptions of (predominantly American) digital speculators. Even a cursory semantic analysis of the noughties’ favourite verbal noun reveals quite how problematic our reliance on these terms became. How did we ever imagine that ‘disruption’ was an unquestionably good thing? 2. The fact that there was (and to an extent still is) a perception of money around social media studies didn’t help. In a period when funding was being cut generally across the humanities, social media studies seemed exciting and lucrative, drawing cash from both governments and industries, a safe bet for an academic strategist. The trend persists, in truth. Even the title of

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this book implies that there is something unified or singular about digital media technologies, and that this makes it logical to study them all in a similar way (in precisely the way I am proposing, obviously). 3. The word ‘platform’ was in vogue for a time in social media studies and rose to prominence, I think, in large part because of the inadequacy of other terms. Platform was meant to capture the broader and more complex ecosystems of web-life, which frequently integrated sociality and commerce, supported news media and recreational gaming and, in general, defied established categorisation. These systems were too extensive to be called websites and were delivered through too many different channels; they could not be called software because their code bases were networked and huge and largely private. Furthermore, their parent companies had successfully sold an idea that they were operating, essentially, as utilities, providing infrastructure upon which social complexity was establishing itself. Eventually, push back against this idea and a growing political will to regulate these companies made the platform label seem somewhat more problematic. 4. It is a fairly common refrain, now, that the Big Data hegemony is neither neutral nor necessarily desirable. It feels like a long time ago that editor of Wired magazine evangelised that big data would end theory: ‘Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves’ (Anderson, 2008). In this view, critical work becomes obsolete and science turns to Google for answers to its most pressing problems. A decade later and the same magazine is probing inherent and inescapable problems with data driven decision making, discovering along with the rest of civil society that data and algorithms now influence countless social systems and that the results can be problematic: ‘algorithms have pushed their way into other aspects of the criminal justice system—from bail and sentencing decisions to diverting people from jail to mental health services—they’re now creeping their way into everyday police work’ (Lapowski, 2018). 5. If we want to explain difference between applications, we must account for how those spheres have interacted historically, and we must do so with a dual perspective, recognising that logics are shaped through communication between the internal-facing system and the external-facing system. This type of analysis can only work because history is a recollection of events and events shape systems. Generally speaking, history recalls events that were particularly influential, which means that they shaped systems in ways that were notable or dramatic (though ‘generally speaking’, of course, covers multiple sins of omission, negligence, manipulation and silencing).

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References Abbate, J. (2000). Inventing the Internet. Cambridge, MA/London: MIT Press. Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired, 16(7). Retrieved from http://archive.wired. com/science/discoveries/magazine/16-07/pb_theory Beer, D. (2009). Power through the algorithm? Participatory web cultures and the technological unconscious. New Media & Society, 11(6), 985–1002. https:// doi.org/10.1177/1461444809336551. Berners-Lee, T., Cailliau, R., Loutonen, A., Frystyk Nielsen, H., & Secret, A. (1994/2003). The World Wide Web. In N. Wardrip-Fruin & N. Montfort (Eds.), The new media reader. Cambridge, MA: MIT Press. Berners-Lee, T., & Fischetti, M. (2000). Weaving the Web: The original design and ultimate destiny of the World Wide Web by its inventor. New York: Harper Collins. Berry, D. (2011). Messianic media: Notes on the real-time stream. Stunlaw. Retrieved from http://stunlaw.blogspot.com.au/2011/09/messianic-medianotes-on-real-time.html Boyd, D., & Crawford, K. (2011). Six provocations for big data. Paper presented at the A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, Oxford Internet Institute. Bruns, A., & Moe, H. (2014). Structural layers of communication on Twitter. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and society (pp. 15–28). New York: Peter Lang. Bullock, S. (2008). Charles Babbage and the emergence of automated reason. In P. Husbands, O. Holland, & M. Wheeler (Eds.), The mechanical mind in history. Cambridge, MA: MIT Press. Cailliau, R., & Gillies, J. (2000). How the Web was born: The story of the World Wide Web. Oxford, UK/New York: Oxford University Press. Castells, M. (2004). Informationalism, networks, and the network society: A theoretical blueprint. In M. Castells (Ed.), The network society: A cross-cultural perspective. Northampton, MA: Edward Elgar. Castells, M. (2010). The rise of the networked society (2nd ed.). Oxford, UK: Blackwell. Connolly, D. (2000). A little history of the World Wide Web. Retrieved from http:// www.w3.org/History.html Coupland, D. (2010). Marshall McLuhan: You know nothing of my work. New York: Atlas & Co. Erickson, M. (2012). Network as metaphor. International Journal of Criminology and Sociological Theory, 5(2), 912–921. Evans, S. K., Pearce, K. E., Vitak, J., & Treem, J. W. (2017). Explicating affordances: A conceptual framework for understanding affordances in communication research. Journal of Computer-Mediated Communication, 22(1), 35–52. https://doi.org/10.1111/jcc4.12180.

162 

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Feenberg, A. (2005). Critical theory of technology: An overview. Tailoring Biotechnologies, 1(1), 47–64. Feenberg, A. (2017). Critical theory of technology and STS. Thesis Eleven, 138(1), 3–12. https://doi.org/10.1177/0725513616689388. Flamm, K. (1988). Creating the computer: Government, industry, and high technology. Washington, DC: Brookings Institution Press. Fray, P., Pond, P., & Peterson, J. (2017, July 4–7). Digital work practices: Matching learning strategies to future employment. Paper presented at the 2017 ANZCA Conference, Sydney. Fuller, M. (2008a). Introduction, the stuff of software. In M. Fuller (Ed.), Software studies: A lexicon. Cambridge, MA: MIT Press. Fuller, M. (2008b). In M.  Fuller (Ed.), Software studies: A lexicon. Cambridge, MA/London: MIT Press. Gehl, R. W. (2011). The archive and the processor: The internal logic of Web 2.0. New Media & Society, 13(8), 1228–1244. https://doi. org/10.1177/1461444811401735. Gibson, J. J. (1979). The theory of affordances. In J. J. Gieseking, W. Mangold, C.  Katz, S.  Low, & S.  Saegert (Eds.), The people, place, and space reader. New York: Routledge. Hassan, R. (2009). Empires of speed: Time and the acceleration of politics and society. Leiden, Netherlands/Boston: Brill. Hassan, R. (2018). There isn’t an app for that: Analogue and digital politics in the age of platform capitalism. Media Theory, 2(2), 1–28. Heidegger, M. (1954). The question concerning technology. In The question concerning technology and other essays. New  York: Harper Perennial. (Reprinted from: 2013). Hindeman, M. (2010). The myth of digital democracy. Princeton, NJ: Princeton University Press. Hodges, A. (2014). Alan turing: The enigma. London: Vintage Books. Hughes, T. (1983). Networks of power: Electrification in Western society, 1880–1930. Baltimore/London: The John Hopkins University Press. Joinson, A. N., & Piwek, J. (2013). Technology and behaviour change: For good and evil. In Social impact of technology. New York: STI Press. Katz, E., & Liebes, T. (2007). ‘No more peace!’: How disaster, terror and war have upstaged media events. International Journal of Communication, 1, 157–166. Kleinrock, L. (1964). Communication nets: Stochastic message flow and delay. New York: McGraw-Hill. Lapowski, I. (2018). How the LAPD uses data to predict crime [Security]. Wired. Retrieved from https://www.wired.com/story/los-angeles-policedepartment-predictive-policing/

6  AUTOBOTS ASSEMBLE 

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Leiner, B., Cerf, V. G., Clark, D. D., Kahn, R. E., Kelinrock, L., Lynch, D. C., et al. (2009). A brief history of the Internet. Computer Communication Review, 39(5), 22–31. Lewis, J. (2005). Language wars: The role of media and culture in global terror and political violence. London: Pluto Press. Lewis, J., & Best, K. (2003). The electronic polis: Media democracy and the invasion of Iraq. Reconstruction: Studies in Contemporary Culture, 3(3). Retrieved from http://reconstruction.eserver.org/Issues/033/lewisbest.htm Manovich, L. (2001). The language of new media. Cambridge, MA/London: MIT Press. McLuhan, M. (1964). Understanding media: The extensions of man. Cambridge, MA: MIT Press. Miles, A. (2007). Network literacy: The new path to knowledge. Screen Education, 45(Autumn), 24–30. Misa, T. J. (2011). Leonardo to the Internet: Technology & culture from the renaissance to the present (2nd ed.). Baltimore: The John Hopkins University Press. Morozov, E. (2013a). The Meme Hustler: Tim O’Reilly’s crazy talk. The Baffler, No. 22 Modem and Taboo. Retrieved from http://www.thebaffler.com/past/ the_meme_hustler Morozov, E. (2013b). To save everything, click here: The folly of technological solutionism. New York: Perseus Book Group. Oliver, M. (2005). The problem with affordance. E-Learning, 2(4), 402–413. Pond, P. (2016). Software and the struggle to signify: Theories, tools and techniques for reading Twitter-enabled communication during the 2011 UK Riots (Doctor of philosophy). Melbourne, Australia: RMIT University. Pond, P., & Lewis, J. (2019). Riots and Twitter: Connective politics, social media and framing discourses in the digital public sphere. Information, Communication & Society, 22(2), 213–231. https://doi.org/10.108 0/1369118x.2017.1366539. Pond, P., Peterson, J. F., & Fray, P. (2019). Digital work practices and capabilities: Developing an affordance scaffold for teaching journalism students. Media Education Research Journal, 9(1), 75–95. Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier. Cambridge, MA: MIT Press. Rieder, B. (2012). What is in PageRank? A historical and conceptual investigation of a recursive status index. Computational Culture: A Journal of Software Studies, 2. Retrieved from http://computationalculture.net/article/what_is_in_ pagerank Tukey, J.  W. (1958). The teaching of concrete mathematics. The American Mathematical Monthly, 65(1), 1–9. https://doi.org/10.2307/2310294. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

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van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and Communication, 1(1), 2–14. Wellman, B., Quan-Haase, A., Boase, J., Chen, W., Hampton, K., Díaz, I., et al. (2003). The social affordances of the Internet for networked individualism. Journal of Computer-Mediated Communication, 8(3), 0–0. https://doi. org/10.1111/j.1083-6101.2003.tb00216.x. Weltevrede, E., Helmond, A., & Gerlitz, C. (2014). The politics of realtime: A device perspective on social media platforms and search engines. Theory, Culture & Society, 31(6), 125–150. https://doi.org/10.1177/ 0263276414537318. Williams, R. (1975). Television: Technology and cultural form. London: Routledge.

CHAPTER 7

The Political Public

The digital system assembles according to a set of operational logics. I have relied on the work of José van Dijck and Thomas Poell to describe four of these logical operations and I have argued that these four examples are ‘descended’ from two super-logics: automation and networking. Datafication is the tendency to record every interaction, to translate those records into representative ‘metrics’ and, by extension, to quantify vast swathes of the social experience. Programmability is the systemic automation of decision-making, based largely on statistical interpretation of data through code, which is itself a product of computational necessity and human politics. Programmability makes demands of the data—it requires variables in specific types with operable attributes—and it always demands more data. The demand for more data drives connectivity: existing resources must be mined more deeply or new resources must be found. These logics drive networked capitalism, an expansive, extractive data machine, converting human activity into advertising revenue. Interactions make data and so the machine always demands more interaction and rewards it in turn. Complex human engagements are datafied into easily quantifiable measures of ‘connective-success’. Popularity is the key measure of worth: more popular is more expansive, more people make more data. The four logics shape the production of meaning in the system. Popularity is datafied, programmed and promoted for greater connectivity: the better your account ‘performs’ on Instagram, the more Instagram © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_7

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does to help your account perform. The like count becomes a meaningful component of the Instagram post and the ‘ratio’ becomes a discursive critique on Twitter—datafied-popularity metrics are encoded into digital text and become familiar signifiers with status. Twitter users established a practice of using the ‘#’ symbol to denote topicality; Twitter collects data on hashtag use, automates linking between hashtags, records the popularity of different hashtags and programs its trending algorithms on this basis. There is a constant negotiation of logical precedent between system and text. For systems, however, negotiation is multi-directional. Systems interact with other systems in 360° panoramic-complexity—which is, of course, precisely the dynamic that system interactionism is supposed to reveal. Obviously, this makes life difficult for a researcher: an infinite number of events potentially bear influence on a phenomenon. Even in a modern ‘big data’ environment, we will never be able to capture or to describe every influential interaction. It is a scalar impossibility. So, as we have discussed already, our best bet is to seek those interactions that appear probabilistically more influential than others, given the context and the scope of our study. We focus our research on interactions between the digital and the political systems because we start with the assumption that these are the interactions that are most likely to influence meaning production in the digital-political system combined. At the same time, however, we are still aware that digital systems are interacting elsewhere. In the previous chapter I spent a little time sketching the dynamics of interaction between the digital system and neoliberal economics, the logical priorities of which arguably do far more to shape the political system than anything else. We can observe how the programmable logics of social media, for example, are tweaked to maximise corporate revenues and how this, in turn, drives the monetisation (and quantification) of popularity with potentially dramatic consequences for politics. If it’s not clear already, this framing should underline again just how important the concept of logics is for analysing interactions between systems. Logics are our evidence that chaos is being converted into complexity because certain types of interaction are happening repeatedly in the same way. This is crucial because it means that the events that we observe most often are most likely to be significant in shaping the logical relationships between systems.1 The observation of repetitive interaction events makes it possible to describe logics within systems and these logics make it

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possible to analyse relationships between systems. We can (only) observe interactions but we interpret logics. Clearly it is important to observe all that we can—as Latour urges with ANT, the only direction is more description (Latour, 2004)—but it a curious idea to me that we should empty our analytical minds completely before doing so. Latour’s argument, which is that good descriptions do not need explanations, should not mean that we completely decontextualise our observations. If an event repeats and becomes fundamental to a particular logical operation, then its role in that logical structure is an important part of its description. Logical knowledge, then, is an important part of effective contextual description, and we have volumes of logical knowledge, discovered through countless modes of enquiry, written down in ways that should inform our current observations. That’s why system interactionism urges a methodology that combines observation with logical interpretation—recall that the logics of becoming are the key, temporalised difference between this approach and ANT. Logics are what describe patterns of interaction in time, and so we will always be trying to order and organise logical ideas: historical knowledge contextualises observation, which informs prediction. That means that, before we can begin to observe interactions between digital and political logics, we have to establish the historical precedents that inform our observation of politics. We are becoming familiar with the ways in which the digital system is conditioned to work, but what about politics and the people who practice it? There has been an enormous amount written on the complex relationship between digital media and politics and a lot of the commentary, which was excitedly optimistic a decade ago, has now soured somewhat. When I began my PhD in 2012, much of the analysis was preoccupied with the Arab Spring, and Facebook and Twitter were being credited with enabling an informed, liberal democratic turn (e.g. Howard et  al., 2011). There had been a series of protests by young people in authoritarian countries, and western commentators seemed excited that (western) technologies were driving these events. In Moldova in 2009 it was text messaging (Shirky, 2011) and then, in the same year, the US government famously asked Twitter to delay scheduled maintenance because activists were supposedly using the micro-blogging service to coordinate protests in Iran (Musgrove, 2009). Facebook and YouTube were widely credited with influence in Egypt in 2011 (Starbird & Palen, 2012) and so on it went, spawning an entire academic genre. Paper after paper sought empirical

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evidence that social media was indeed being used by activist publics to make meaning and maybe even coordinate political action, plenty found it, but it’s tough to make a case that the political environment has improved much in the past decade. Throughout 2019 about a quarter of the Hong Kong population protested regularly in defiance of their local government and its Chinese overlords—a movement that continues despite considerable risk of violence and an incrementally more oppressive response. The discussion of technology in relation to these protests is markedly more cautious than once it would have been, and so it should be. Journalists are as interested in protestors’ attempts to avoid technological surveillance as they are in social media chatter. The past five years offer countless depressing examples of global technology corporations breaking (for profit) fragile political systems that they seem not to have understood.

If Politics Were Simple: The Deliberative Utopia Much of the early analysis of the ‘political potential’ of the internet involved a comparison between the logics of social media and the dynamics of established political process (e.g. Barber, 2006; Benkler, 2006; Buchstein, 2002). Often, these debates reduced to arguments over two different logical sets: one type was organisational (social media would connect people and help them mobilise) and the other was communicational (social media would inform people and informed people would want self-­ representation). Even as a student, I recognised that organisational analyses, also called ‘action-orientated models’, tended to ‘assume that Internet technologies confer on their users some of the properties of the underlying network architecture’ (Pond, 2016, p. 97).2 As we have established, it is far too simplistic to assume that any system will map its logics directly on to another, and much of this early organisational analysis was decidedly deterministic. I found the discursive or communicative arguments far more persuasive, even if most depended on the work of an ageing German philosopher, who famously had next to no interest at all in the internet. It’s nigh on impossible to engage with early internet studies without confronting Habermas and his theories of the public sphere, deliberative democracy and communicative action. Time and again these ideas were used to explain how the web could empower citizens and invigorate democratic processes (Rheingold, 2000).

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I was drawn to these communicative democratic models, because I reasoned that the internet and digital media were better evaluated as communicative systems, whose primary influence was to shape how we saw each other and our places within the world. Habermas located democracy within a communicative context, making democratic success a product of informed reasoning, ideas which were readily transferable to networked media where citizens could create and share their own texts. Internet technologies supposedly had the potential to liberate publishing from media gatekeepers and established interest groups. By lowering communication costs and by opening access to media platforms, the internet was meant to enable all citizens to participate in (and contribute to) deliberative discourse, invoking reason and seeking consensus. As Dan Gillmore (2004, p. xviii) wrote: ‘The ability of anyone to make the news will give new voice to people who’ve felt voiceless—and whose words we need to hear’. Habermas proposed normative theories—explanations of how democracy should work in an idealised context, where deliberation and reason are guiding principles. The validity of such a context, including its origin in Enlightenment thinking, has been debated extensively, and it is not a debate that needs to be reignited here (e.g. Love, 1989). Rather, we can note that these ideals have mattered as we have attempted to define democracy, to conceptualise ways of conducting politics and, indeed, when constitutions were written, and parliamentary procedures developed. Deliberation is central to the Habermasian ideal. In simple terms, we can think of it as the type of communication necessary for two political actors to reach a decision through reasoned consensus—that is, the type of decision that both actors can accept as justified given what they know and what they believe to be true. Habermas argued that governments must engage with citizens in deliberative discourse if they are to achieve democratic legitimacy: a discourse-theoretic interpretation insists on the fact that democratic will-­ formation does not draw its legitimating force from a previous convergence of settled ethical convictions, but from both the communicative pre-­ suppositions that allow the better arguments to come into play in various forms of deliberation, and from the procedures that secure fair bargaining processes. (Habermas, 1994, p. 4)

In order for communication to be deliberative, participants need to be able to engage in communicative action (Habermas, 1984; Jacobson &

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Storey, 2004). According to Jacobson and Pan (2008, p. 13), communicative action is possible when discourse satisfies both Habermas’ validity claims and his ideal speech conditions. The validity claims describe ‘claims regarding the truth, appropriateness, and sincerity of each and every act of speech, even lies’. In other words, in order to pursue productive discourse, communicators must be free and able to ask the following questions of the subject being debated—and of claims about the subject made by their fellow communicators. They must be able: . To question what is comprehensible to them; 1 2. to determine what is true in light of their individual and shared knowledge; 3. to assess what is sincerely or truthfully stated; and 4. to decide what is a moral or an appropriate statement given the communicative situation. If disagreements arise over a validity claim, then reasonable deliberation requires three ideal speech conditions: . Equal and symmetric opportunities to contribute; 1 2. the ability to raise any proposition or position; 3. a ‘full and equal’ consideration of propositions and positions raised. (Jacobson & Pan, 2008, p. 14) Now, obviously, I cannot hope to do justice to the complexity of Habermas’ preconditions for ideal speech (or to the debate around the normative value of each condition). I simply want to introduce the idea to help illustrate how a normative political system might work if it were operating ideally to produce meaning through deliberative differentiation. Put another way, communicative action establishes conditions for speech that the logics of system differentiation must somehow satisfy. In other words, if it were working normatively, components within the political system would be equally interactive, symmetrically ordered, with the same perspective within the system and access to resources. In effect, these ‘ideal’ logical conditions would produce normatively valid differentiation—communication that was informed, truthful and comprehensible to all systemic components.

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Does this happen on the internet? Does it happen anywhere? One of the persistent criticisms of normative theory has been that it is too idealistic and too prescriptive. It describes a set of conditions that are so implausible that they cannot even be applied in an aspirational sense (Stahl, 2013). If ideal speech is no more attainable that universal peace and charity, then it is no more useful for effecting change than a gentle plea to the wealthy to be just a little bit more generous with their money. Nevertheless, there persisted for a long time a strand of argument that curiously mixed Habermas’ Enlightenment pursuit of reason through deliberative exchange with the postmodern idea that the internet could liberate users to navigate and construct their own identities. The assumption was that this ability to navigate the self, and to re-identify oneself, enabled all citizens to engage in the public sphere on an equal footing. Sherry Turkle, writing in Wired, explained that ‘There are many Sherry Turkles’, one of whom is ‘the cyberspace explorer, the woman who might log on as a man, or as another woman, or as, simply, ST’ (Turkle, 1996). Much like in Habermas’ coffee houses—his Tischgesellschaften of eighteenth-­century France (Habermas, 1991)—on the internet there ‘was disregard of social status, a fundamental parity among all participants such that the authority of the better argument could win out over social hierarchy’ (Dean, 2001, p. 244). On a message board or in a chat room, it did not necessarily matter if a user was the company CEO or the janitor— social credentials were stripped away, and in the pursuit of reason ideas could triumph on merit. One way of summarising these arguments is to say that scholars believed that the internet would democratise the word—messaging, information and argument would become liberated, decontextualised from the power structures and perfidy of ownership and class. Decisions would be reached on the strength of argument alone. Obviously, things have not worked out like that, and so we must transition from our starting position—the ideal set of logics for a political system—and reflect upon how these normative logics were applied in practice. Why did the internet not deliver deliberation?

Messy, Complicated Politics The popularity of the information liberation thesis probably says more about modernity’s techno-philia than it does about critical political theory at the turn of the millennium. Looking back on these second-phase

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debates in internet studies, it’s interesting to note how incredibly complex disagreements were cast in highly functional or mechanical terms. Free information would create an enlightened and critical population, or it would overwhelm limited human brains causing them to spin in confusion. The network would transform society in its egalitarian image, destroying hierarchies and mobilising the masses, or it would do no such thing because determinism is debunked. It can still be tempting to frame the debate around communicative politics in this way, essentially as a question of deliberation versus confusion, but this is making exactly the mistake that this book is supposed to arguing against: the dismissal of complexity. It is highly unlikely that people, all of whom are quite clearly still embedded in power structures, class conflicts, fights against marginalisation (or, conversely, fights to maintain their hegemony), will forget all of that and behave ‘ideally’ as soon as they go online. Imagine the rarefied human behaviour required for textual liberation to ‘supercharge’ deliberation. Imagine a public, with sudden and unprecedented access to massive amounts of information, acting responsibly, choosing to deploy that information in pursuit of reasoned enlightenment. Such was our collective faith in power of technology, apparently, but political publics and the people who constitute them are far more complicated than that. That complexity reveals itself in innumerable ways, and although I intend to focus on communicative complexity here, that should not be read as a denial (or ignorance) of the many other ways in which people are politically complex.3 In fairness, it’s disingenuous to pretend that the internet was welcomed universally by political theorists drunk on techno-determinism. It wasn’t that simple, of course. There was plenty of concern that information volume made deliberation—the process at the very heart of communicative democracy—impossible (e.g. Buchstein, 2002). Theorists worried that citizens may not be able to cope with the enormous amount of information that was about to be dumped upon them. How could anyone read it all, verify it all, process it and make use of it in the pursuit of reason? Under such circumstances, communicative action would be incredibly difficult. Furthermore, information circulated at unprecedented speed, which created communicative conditions that were hardly suited for ideal speech. Above all, communication on the Internet is fast … democracy is a process based on deliberateness. It is about slow and prudent movement. … Democracy is not just about collective decision making. It is about ­deliberate

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collective decision making. Deliberation is absolutely essential. The difference between the tyranny of the majority and real democracy is deliberation. (Barber, 2006, p. 7)

Speed is an important consideration in the conception of the political process. The steps involved in normative deliberation take time to perform. It takes time to process the validity claims: time to receive and to comprehend; to reflect; to evaluate; to identify disagreements; and to deliberate. Concerns about information overload assume that speed stresses the deliberative period for all users to the point that communicative action becomes impossible (Pond, 2015). These worries were no doubt exacerbated by the fact that early empirical studies had failed to demonstrate that meaning was being produced in digital media systems in quite the way that was being promised. To return to Salon’s discussion groups: from the standpoint of the public sphere, the discussions seem, at best, a kind of banal content enabled by a software program installed so as to draw in consumers and advertisers or, at worst, a set of irrational and often demeaning rants of the privileged few against a disenfranchised many. (Dean, 2001, p. 253)

In hindsight and acknowledging the enormous challenges that complexity presents, especially during periods of rapid change, it’s unsurprising that communicative action, with its checklist of validity claims and ideal speech conditions, was appealing for theorists seeking a discursive interpretation of technological influence. As long as we could find evidence of deliberative discussion in chatrooms, blogs and social media channels, we could advance an argument that digital systems were, in some way, inherently democratic. Such an approach ‘translates encompassing, complex and amorphous questions about democracy into more manageable and precise questions about the communicative content of individual tweets’ (Pond, 2016, p. 140). In other words, in order to assess the influence of one system, we dramatically simplified the operation of another. The democratic-communicative potential of these systems becomes a simple question of informational liberation versus informational overload. Deliberation assumes that two actors will reach consensus given enough information and appropriate conditions in which to deliberate. As we have established, though, difference is an inherent part of signification,

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meanings are always contested and so in many ways normative deliberation ‘as practice’ is less a rarefied type of communication and more a contradiction to it. The Habermasian scholars who (continue to) seek deliberative moments on the internet are right to apply a communicative political model—it is far more straightforward than an action-orientated approach, which requires a heap of translational work to convert material logics into communicative events. However, they err if they assume that the logics of deliberation are straightforwardly present in a communicatively complex world.

Not Deliberative, Not Gentle What do I mean by communicative complexity? What does discourse-­ centric political theory look like if it’s not deliberative idealism? In some respects, we have covered this ground already. Communication is defined by the struggle to signify, and it is an instrument of difference, both within the political system and when politics interacts with hypertext. If we are committed to an interactionist analysis of digital-politics, then these are exactly the dynamics that we need to consider. Meaning is an organising force within society, which makes meaning a precious resource worth fighting over. Cultural theorists first realised this through their criticism of structuralism and effects-driven communication models. Stuart Hall argued that the ‘degree of reciprocity between encoding and decoding moments … is not given but constructed’ (Hall, 1980, p. 136). In other words, communication is more a process of interpretation (and transformation) than transmission. ‘What are called distortions or “misunderstandings” arise precisely from the lack of equivalence between the two sides in the communicative exchange’ (Hall, 1980, p. 131). The struggle to signify is thus a struggle to reproduce constructed positions in such a way as to influence the decoding moment—to impose one meaning structure upon another (Lewis, 2005). Deliberation assumes that words have roughly the same meaning for everyone who uses them, that facts hold more currency that opinion, that truth can be established and that, once established, it will impress itself upon communicators. Experience shows us that political communication is invariably more complex and more interactive. This complexity was elucidated by Roland Barthes, who argued that meaning accumulates over and above structuralist signification through a process he called connotation—essentially the context-specific layering of meaning on top of an

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original association (Barthes, 1988). What this layering does, of course, is allow for ideological, political and psychological meanings to exist alongside—and sometimes take precedent over—‘literal’ interpretations (Lewis, 2008, p. 115).4 This is not always a pleasant process. Different meanings struggle for primacy in the mediasphere as social actors try to frame events in terms of their preferences and prejudices. Language wars is Jeff Lewis’ concept for the ideological, emotional and psychological struggles to attach meaning to signifiers (Lewis, 2005; Turner, 1996). It incorporates the raw reality of political power relations into signification and then fixes signification on the fluorescent screen of the modern media. For Lewis, democracy is born from a desire to avoid violence, but it does not dispel violence, rather it co-opts it into language. ‘Violent complexity, therefore, is inscribed in the very raison d’être of the modern state’ (Lewis, 2015, p. 181). democracy has been framed as a discursive mechanism which subsumed violence within the interests and ideology of specific social groups, most particularly those that had vested interests in individual prosperity, capital and commerce. In the progress of democracy, however, violence didn’t disappear; it was merely transferred into the institutional parentheses of ‘responsible’ government while simultaneously being recoded through social interaction and new modes of mediation. (Lewis, 2005, p. 248)

This reframing of politics as violence casts the mass media (and now the digital media also) as agents of war: being ‘a cultural conduit and bearer of information, the global networked media is profoundly implicated in modern terrorism’ (Lewis, 2005, p.  53). Contemporary discourse is deployed primarily to legitimise power, and so the signification acts that produced that discourse are themselves expressions of power (or for power), situated always within complex socio-cultural tensions. What’s more, the media itself, neoliberal, consumerist, predominantly white and male, are both owners and agents of a particular type of power that reasserts itself through oppression and violence, hand-in-hand with nationalist imaginaries (e.g. Lewis, 2015; Samuels, 2009). Please note, I am not proposing that politics should be interpreted as a binary expression of our collective best and worst impulses. I do not believe that we operate either as an idealised Athenian collective, participatory, deliberative and rational, or that we are a hollowed-out shell of a body politic, beaten dumb by corporate rhetoric. I reason that full

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subscription to either view is a little extreme and risks a reductive simplification of obvious complexity. I do, however, think that it is important to hear the type of media criticism embodied in language wars, which clearly has precedent in the work of the Frankfurt School and is currently publicised by writers like Noam Chomskey and Slavoj Žižek. It is important for two reasons. First, because it exposes the fallibility of rationality and deliberation in the face of crude signification power and, second, because it implicates media technologies and media industries at the heart of the political system. Of course, the media and politics were intertwined long before the internet was invented. There have been countless books charting the history of the printed word, its deployment in the service of political power, first by the church and later by the state, and the disruption that new media technologies caused these established hegemonies. Many of these books have heralded moments of technological invention precisely because disruption can appear liberating from a certain perspective. Perhaps most famously, the invention of the printing press and moveable type coincided with Martin Luther’s challenge to the Catholic Church and a continental explosion of apostate writing. Benedict Anderson (1991) documented the many different ways in which writing and print media contributed to the realisation of the national state in the collective imaginaries of previously disparate communities. Time and again though, technologies that appear liberating are also manipulated and deployed in ways that serve the interests of their owners. Propaganda campaigns probably offer the most visceral evidence of media marshalled to impose meaning on populations, but decades of media criticism have produced countless examples of both insidious and expansionary manipulations. The relationship between media and politics is neither linear nor simple nor can it be classified in simple binaries. Technologies may be both liberating and repressive in different places and at different times. Rather than dwell on individual cases that may or may not point to a brighter political future, we should recognise that both the media and political collectives are individually and interactively complex. The issue is that, as Lewis (2015) argues, the complexity itself can be oppressive. If we cannot comprehend the convoluted (and often hidden) negotiations for power and influence that affect us, how can we hope to influence them? As social systems have exploded into global complexes, many of our more intimate and reassuring mechanisms for meaningful differentiation have been reconfigured toward consumerism and simulacra. This the media space in

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which politics is performed. Within such a space, it is little wonder that brute assertions of power can prove so effective. If we are always and everywhere challenged by complexity, thuggish signification can seem both reductive and reassuring. Language is inescapable here. In material terms, it is easy to imagine how the printing press transformed a scarce and closely horded resource (text itself) into a public vehicle for social and political imagining. The influence extends well beyond the material, though. In 1984, George Orwell animated a simple but incredible powerful idea: a truly tyrannical state prevents its population from imagining an alternative. In the novel, the mega-state Oceania deploys incredible resources to rewrite history and to spread propaganda—to impose on its populace, through repetition and coercion, an inescapable reality. More than this, though, it seeks to restrict the individual’s ability to make individual meaning. Orwell was unrelenting in his delivery of this message. The ruling party has its own language, Newspeak, with a limited grammar and vocabulary, so as to limit the capacity for free expression. It insists on Doublethink, or the acceptance of contradictory beliefs, and the submission to irrational reality, the defeat of sense and the logical sign system. Constant surveillance ensures that Thoughtcrime is immediately punished by the Thought Police—the Party seeks total control over the sign system, signification is heresy. It’s hardly a surprise that events of recent years have had commentators and journalists reaching for 1984 to help them explain modern politics. There seems little doubt that politicians around the globe have learned crude lessons from Donald Trump. At the same time, the idealised fourth estate is staggering senselessly, doubtless bruised by a sustained political assault, but also weakened by acutely unfavourable market conditions and several self-inflicted wounds. The role of the journalist as professional purveyor (and arbiter) of truth in the public sphere seems like a quaint anachronism. The issue is that democracy is conceptually idealistic and practically nihilistic. The media is where this tension unravels, never certain in its intent, caught between self-idealisation (with its Fourth Estate rhetoric, speaking truth to power) and the grubby sale of Trumpian spectacle.

Extreme Mediatisation What can we say about the influential logics shaping our politics in 2019? We are faced with a vast field of interactive publics, some local, some national and some international; all different and all emergent, but also all

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conditioned to a greater or lesser extent by a century of electronic media. It would be hard to make a case, I think, that politics is meeting its normative expectations right now, or that political language is differentiating in a way that faithfully represents any sense of shared reality. At the risk of repeating the analysis from Chap. 1, I think we can say that there is a general consensus that politics in 2019 is a fairly wild ride. It’s mid-October as I write this chapter, which should not be a particularly explosive time in the political calendar, and yet in the table below are some of the ‘stories’ making the headlines this week. It’s not an entirely random sample—these are the stories that have caused me particular despair this week—but most weeks this year would provide a similarly crowded gallery of the grotesque and despondent. Each headline reflects a crude political struggle to assert meaning. What’s truly remarkable, though, is how in each case that struggle is almost entirely divorced from any sense of sincerity or truthfulness, morality or propriety. Headline

Source

Date

Lede

Democrats Daily ‘completely insane’: Telegraph Trump

31 October 2019

Trump defends Syria decision by saying Kurds ‘did not help us with Normandy’

The Guardian

10 October 2019

Boris Johnson Metro UK suggests Brexit is to blame for thigh-­ grabbing allegations Extinction BBC.com Rebellion ‘lose control of fake blood hose’

1 October 2019

US President Donald Trump has taken aim at his rivals at a raucous campaign rally in the US deep south, saying the Democrats have ‘gone completely insane’ in their move to impeach him over a phone call with the president of Ukraine. Donald Trump defended his decision to withdraw US troops from Syria and enable a Turkish offensive against US-backed Kurdish fighters in the region by noting the Kurds did not fight alongside the US in the Second World War. The Prime Minister has labelled mounting scrutiny of his private life a possible attempt to ‘frustrate’ Brexit.

3 Oct 2019

Climate change activists’ plan to spray fake blood on the Treasury did not get off to the best start. (continued)

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Headline

Source

Date

Lede

Venezuela wins seat on UN human rights body despite fierce opposition

Sydney Morning Herald

18 October 2019

Mark Zuckerberg flounders before Congress

The Washington Post

25 October 2019

Venezuela won a contested election for a seat on the UN Human Rights Council on Thursday despite a campaign by over 50 organisations and many countries opposed to Nicolas Maduro’s government and its rights record. To hear Mark Zuckerberg tell it, Facebook’s interest in setting up the digital currency Libra comes from a place of selflessness, of concern for the world’s poor and underserved. ‘There are more than a billion people around the world who do not have access to a bank account’, he piously told the House Financial Services Committee early in his Wednesday testimony about the controversial project: ‘The current system is failing them.’

In case we need more evidence that this is happening, let us focus for a moment on Donald Trump, iPhone in tiny hand, thumb poised and ready to tweet, straddling American politics like Alexander over the Gordian knot. There are few examples more illustrative of how political signification can be beaten dumb in the modern, global mediasphere. Trump’s language repeatedly calls on crude simulacra cast in bullying defiance of Enlightenment discourse and deliberative reason. The familiar insults and the child-like tropes discussed briefly in Chap. 1, and ever present on cable news in 2019, are violent and repetitive challenges to meaning: fake news, radical socialists, deep states, witch hunts and on and endlessly on. Trump is catchphrase politics, a ‘blank sucking nullity’ to borrow a phrase from the perspicacious Trump observer, David Roth (2017). For instance, during the 2016 presidential campaign, Donald Trump stood at rallies and declaimed that only he could ‘drain the swamp’ in Washington. Pundits and some voters, perhaps, interpreted it to mean that Trump would reduce corruption in Washington, which political writers had been calling ‘the swamp’ infrequently for years—admittedly a pretty niche usage. Such an interpretation was demonstrably laughable, however, and beyond repeating the slogan to chanting crowds, Trump never demonstrated any realistic intention to drain corruption from anywhere. As far

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as I’m aware, there is no empirical evidence that Trump has ever been particularly opposed to corruption, but the value of that phrase for Trump never depended upon its objective credibility. Indeed, there was hardly an attempt to root the slogan in any sort of practical or ideological policy agenda. What purpose did it serve then, and what did the words mean to the MAGA crowds who chanted them so enthusiastically alongside the (many) other Trumpian mantra? Trump’s presidency has been defined by such disregard for the norms and restraints of political signification. We can only guess at what ‘drain the swamp’ meant to individuals in crowds at Trump rallies, but we can be clearer about the purpose it served. Like so many Trumpisms, the slogan is an empty signifier: there is considerable doubt that it means anything specific even to the speaker, who seems to repeat these catchphrases more for sub-emotional gratification rather than for specific political or strategic purpose. Receivers have huge latitude in how they interpret the phrase: it is a vessel for frustration and recalcitrance, part of the Trumpian dialect for ‘othering’. He specialises in such gaudy signifiers for the angry and disaffected. The purpose it serves, both in respect to Trump’s narrow political interest and the wider Republican imaginary, is relatively clear: it is a grievance that reasserts difference, a war cry of a powerful public, even if it masquerades as something threatened or disaffected. ‘Drain the swamp’ is blunt-force media-politics: it’s not meant to be informative, nor to persuade or to reason. It has no specific referent in Washington politics; it is purely for consumption in the media market. Another example: the Democratic Congress has just impeached Donald Trump for trying to bribe Ukraine’s new president into supporting a public smear campaign against a possible 2020 election rival. The Republican Senate has already declared its intent to acquit him and is currently manoeuvring to undermine the trial it must eventually conduct. There is no nope that impeachment will remove or even restrain Trump, so why have the Democrats bothered? This is surely a mediatised political calculation. Even if the Senate does nothing, at least Trump will be surrounded by the spectacle of procedural scandal. Of course, it’s a place that he finds fairly comfortable, it should certainly be familiar and it may even strengthen some sort of bond between him and his core support, but if the spectacle seems serious enough, if enough mud can stick, then it may finally have some effect on a few persuadable voters in the coming election. Republicans are preparing for that same election by rephrasing ‘Make American Great Again’ as ‘Keep America Great’. The sole nod to any

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shared sense of reality in that new phrase is that greatness, whatever it maybe, must logically have returned to America in 2016, when Trump was first elected. Having held the presidency for four years, the internal logic of the statement clearly needs updating, else its promise rings somewhat hollow. Greatness, we suspect, in the eyes of Trump and many supporters, is effectively whiteness or, perhaps, Trump-ness, depending on the president’s variable ability to differentiate between ideology and ego. There is a curious parallel construction with slogans coined during the Brexit campaign, a verbal affirmation of an uncertain proper noun: Brexit and America, two nationalist projects, both threatened by unnamed assailants and a promise of realisation. Meanwhile, in the UK, the ruling Conservative party won the recent general election with the slogan ‘Get Brexit Done’, somehow maintaining a fallacy that Brexit is both wonderful and desirable and yet needs dispensing with quickly (so that Britain can move on to issues with its national infrastructure). It is a vacuous, fallacious slogan, which obscures the truth that Brexit will never be done and will almost certainly dominate the national political landscape for another ten years. Indeed, the history of the Brexit process can largely be told through the vacuous slogans that Brexit supporters have sequentially pumped into the semantic void created by an ill-defined referendum question (and campaign) that did next to nothing to define what Brexit must mean. The government has consistently worked with certain media outlets to impose its preferred meaning of Brexit on to the 2016 referendum result, an interpretation that has largely been shaped by its internal political divisions. What else should we call this process, other than extreme mediatised politics? Through Brexit, and its impatience to achieve it, the slogan speaks to a specific type of frustrated nationalism, with latent racist and xenophobic prejudices and persistent imperial delusions. The Conservatives ally their claims of Brexit stewardship with language that demonises minorities, immigrants and the political ‘other’. The slogan provides no information about how Brexit will ‘get done’, and the Conservatives have not published the strategy that will deliver such success; indeed, the three years of Brexit politics to date, all under Conservative rule, have been remarkable lacking in sense, coherence, honesty or progress towards Brexit. ‘Get Brexit Done’ does not reveal policy, it obscures it. It is a silencing, polarising political dog whistle. It also ignores the inconvenient fact that the Conservatives have been in power for nine years already and that

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Conservative rebels have rejected every form of Brexit withdrawal deal tabled so far in parliament. In Australia, where politics in 2019 has been reduced to a standoff between coal lobbyists and wildfire, Prime Minister Scott Morrison is floundering while vast swathes of the country burn. It does not help, of course, that he famously sat in parliament and waved a lump of coal at his political opponents, amused that they should be so scared of it.5 It is a grotesque irony that the grand ‘climate election’ of 2019 returned a government committed to the expansion of mining and fossil fuel consumption. Either ‘securing your future’ was never meant to cover us against fire damage, or the ‘your’ in question was more exclusive than we realised— you are either mining magnets or own beachfront real estate (either way, it’s helpful to have ready access to a large body of water). Nationalism being younger and weaker in Australia, the climate becomes an existential threat to our imaginary, but climate politics in Australia is defined by noisy inaction—a semantic struggle to define the threat rather than confront it. The political rhetoric, when it addresses the climate at all, serves only to distance politicians and their policy from the consequences of their inaction. As the prime minister obfuscated: ‘it’s important that [at] a time like this of natural disasters, that Australians focus on coming together and not seeking to drive issues of conflict and issues that can separate Australians at a time when we all need each other’ (Remeikis, 2019). Meanwhile, the country has just been ranked worst in a global comparison of climate policy (Burck, Hagen, Höhne, Nascimento, & Bals, 2019). In this fire season, millions of acres, thousands of homes and scores of lives have been lost. I do not think that it’s too crude to suggest that politics has become performative spectacle, where power resides in the ability to control signification through the media. Internationally, political signification is largely unmoored from institutional and procedural democracy. The mediatisation of politics and the public sphere is widely acknowledged (Castells, 2009; Couldry & Hepp, 2013; Thompson, 1991), though some theorists would argue that the interaction between the two systems is not yet so extreme. Of course, we need to be careful not to reduce a complex and multi-layered system to semiotics. I am not arguing that politics can be reduced only to mediatised performance,6 but I do think that there’s plenty of evidence that many of the practices and procedures of democracy are already mediatised. In China, the media and the state are the same system operating with the same logical priorities. In Russia, something

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similar has happened and in Turkey the independent media has largely been destroyed. In Israel Benjamin Netanyahu is facing corruption indictments for his attempts to trade state assets for favourable media coverage. In Europe, media figures either have grasped political power (in Ukraine and Italy, for instance) or are challenging it (as in Austria, where it was Süddeutsche Zeitung that challenged Heinz-Christian Strach’s attempted corruption of another newspaper). No wonder that populism is now being reframed as ‘political style’ (Moffitt & Tormey, 2013). This trend is more insidious than cable news reducing politics to competition (Patterson, 1993) and one way that we sense its destructive influence is through acute changes in political language. In cultural studies, there is a long-held view that the US launched its ‘war on terror’ largely into the semantic mediasphere. The ground wars that it fought were remote and abstract; the soldiers that fought them were professionals not conscripts; the cities that fell and burned were dusty and unfamiliar to western audiences. The initial acts of terrorism and the wars that followed were particular types of communicative violence, the fallout from which caused incredible damage to the collective cultural psyche. Exactly how much did western governments exaggerate the risks to their civilian populations in pursuit of greater surveillance, more military funding, restrictions on civil liberties and bureaucratic secrecy? The evidence that has leaked out is damning (Bamford, 2004; Davies, 2008) and clearly the exponential scaling of risk heralded a new era in political meaning-making. Consider, briefly, the lies that led to war in Iraq, and how much damage that did to the credibility of the political word in the US and in Britain. Nevertheless, until Trump happened, we just about held faith in our Enlightenment rules for signification, expectations clearly expressed in those deliberative models. We had norms for establishing facts, procedures for distinguishing truth from lies and processes for resolving disputes. We acknowledged political ‘spin’ but if a politician was caught lying, there was still an expectation that the political public, often acting through the media, would hold him accountable. As the previous sentence acknowledges, many of these norms and rules depended on the electronic media to function. Scandal can be a media construct: scandals break at cable news desks and unfold through cable news cycles. If we frame it in systemic terms, scandal is the product of a media system focussing its attention (and meaning-making) on a political actor and holding it there until the political system responds. After all, Mass media work ‘to saturate coverage of

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events over a short period of time, slack off, and eventually turn to something else’ (Altheide & Snow, 1979, p. 238). The reaction to Trump suggests that this faith is now broken. Famously, the Washington Post keeps a running tally of the president’s falsehoods or misleading claims (lies to the rest of us) which had reached 13,435 after 993 days of his presidency (Kessler, Rizzo, & Kelly, 2019). It is unsurprising that the integrity of the system-sign relationship has suffered under such a sustained assault from a position of such political and media power. How bad is it? It can be hard to know quite how severe something is while still in the middle of it, but the word ‘crisis’ has been invoked (Belakova et al., 2018). Clearly Trump is an incorrigible liar and fantasist and clearly other media-politicians are aping his language and his disinterest in reason and evidence, but what about the rest of us? Is the entire political public now complicit in a hyperreal dystopia? We must recognise that politics as practiced in 2019 is already digitised: the two systems have been interactive for decades. So, there is no simple classification method here, no origin story that can ground our future analysis. All we can do is identify the publics we want to study and attempt to describe how meaning is being made through cycles of interaction and differentiation. If we assume that the digital and the analogue represent opposite ends of a representational spectrum, where does political meaning-­making sit on that spectrum? How do we feel about the representational integrity of our political language?

Logical Inequivalence In my view, the logical rules of politics barely survived the collision with the electronic mass media system. When systems interact, what is produced is something logically different. Mediatisation has exacerbated the performative aspects of power and destabilised political language. These effects disrupt democracy at its normative core. Complexity challenges comprehensibility, truth becomes uncertain and sincerity is always questionable. Political actors turn away from consensus, even the idea of shared knowledge becomes problematic. So, what happens when this tumultuous politics interacts with the digital system through hypertext? I’ve argued that Trump’s language is a product of media-politics, and in particular a cable news industry that is desperate for spectacle. I think that’s largely true, but Trump is a chimera: he is arguably the first hyperreal, hypertextual politician. He is clearly cable-addled, but he is also inseparable

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politically from his hyperactive Twitter feed. In assessing Trump, we cannot ask if the electronic or the digital media systems adhere to normative political rules, or vice versa, because the political system has changed with him, and those rules have changed as well. In truth, they have been changing for a while. All of the examples I have cited in this chapter involve a media-politics that is already widely digitised. Trumpian rhetoric may have been amplified by dissociation and repetition on the internet, supercharged by the twinned logics of popularity and power, but it also only exists because of the internet. In the complementary logics of mediatised-performance and datafied-popularity, there were always the preconditions for some sort of systemic synthesis. The logics of mediatised politics were already shaped to be receptive to the logics of digital differentiation. Partly, of course, this is because the two media are competing for advertising revenue within a shared political economy, in which audience attention is traded competitively (Fuchs & Mosco, 2016). Trump became politically prominent by feeding spectacle into the most sensationalist channels in this political economy. He built himself a receptive audience first on Twitter and then via the vast network of conservative and right-wing websites. His ability to occupy a cable news screen is evidently correlated to his ability to drive digital ‘traffic’, to direct audience attention, which can then be quantified and sold. This popularity is transformed through multivariable complexity into the enervating logic of the digital economy. So, in a sense, we are all complicit, because it is our attention that it is being packaged and sold, and we apparently have an insatiable appetite for Trumpian spectacle. Surely, though, that is too simplistic. We consume this hyperreal politics, but we also vote for it. We consume all sorts of other content without necessarily wanting to make it manifest in our world (I’m thinking of Love Island and Scandinavian crime dramas, for instance). So, what is happening? How does Trump’s mediatised popularity translate into political power? What are the dynamics and the mechanics of this process? What happens, specifically, when these different systems, already primed, finally interact? I said before that I do not believe that politics can or should be presented as a reductive binary, opposing deliberation and semiotic power, because it is obviously far more complicated than that. These oppositional statements are always appealing, however, because they help us to characterise (at least in outline) distinct logical types: deliberation versus violence; liberation versus repression; and analogue versus digital.

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Luhmann differentiated between communication within a system and communication between systems. Interactions between systems reassert difference, because neither system has full access to the internal logics of its counterpart. Each interaction, then, is information that each system must process internally. Differentiation is situated and meanings are tripartite: there is information (some sort of interaction event, either symbolic or material) and there are two systems each working to interpret that information, to assign it significance according to their internal meaning-­ making logics. Perhaps binaries are appealing because they emphasise logical differences. They remind us that meanings are inherently unstable, because different systems cannot be identically situated. Signification happens in a bizarre non-space, partly within and partly between systems, and there can never quite be equivalency. For some authors, the lack of equivalency between traditional political models and the new signification environment is responsible for many of the more troubling political dynamics that we observe currently. However, equivalency may also be an issue between separate modes of logical differentiation within systems, especially if those systems have undergone a period of rapid change or invention. Robert Hassan (2018) has described what he considers to be the essential characteristics of ‘analogue’ politics, and central to his description is an idea of reflective representation. This is the sense, inherent in the Greek analogon, that our productive constructs should represent objects in the world around us. For most of our history, Hassan argues, our technological inventions had reflective provocations in nature, which enabled inventors to project outcomes in time and space. Airplanes mimic the flight of birds, submarines dive like fish, tractors pull ploughs like oxen and so on. Similarly, language, which is a technology too, invents signs that reflect the world in which they are conceived. The word ‘bird’ functions linguistically because it reflects back into the world faithfully. Similarly, we can liken submarines to fish because we know what both of these things are—the representations follow natural precedents. In systemic terms, we can say that the language system differentiates according to logics that represent the natural world faithfully. We can make sense of the natural world through language because our language produces meanings that reflect meanings in the natural world. The two systems align, more or less. Hassan says that digital systems are not like this—they have no precedent in nature. ‘Having no recognisable analogue in nature means that we do not easily grasp, or recognise, what digital does at the

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technology-human-environment level’ (Hassan, 2018, p.  7). If we refer back to the originating logics of the Turing machine, we can see some sense in the argument. Computers automate the production of mathematical abstraction; they fulfil a rarefied function that few humans can easily conceive. Advances in networking and processing power have powered this abstraction, producing a representational logic that Hassan sees as somehow separate from nature and material reality: ‘Much that was once analogue “natural” in our communicative practice has become digital “atmospheric”’ (p. 8).7 If Hassan is right, then we are dealing with two system-types that are profoundly different in how they represent meaning for differentiation, and we should expect that sustained interaction between them is likely to produce radical change both within and across systems. I have spent two chapters sketching the logical precedents of the two system-sets so that we can explore this difference, and so that we can interpret interactions according to the tripartite communication model. We need to know how systems self-differentiate so that we can read interaction events critically from each system-perspective. That is the task ahead: to decide upon a perspective and a method for observing system interactions in hypertext and then to decode those interactions, particularly from the political perspective, because, after all, that is our area of primary concern. First, we must decide if we agree with Hassan’s assertion that the digital and the analogue are profoundly and incompatibly different domains. We need to decide quite how different they are because, clearly, this will influence our reading of any interactions. Will political decision-making increasingly mirror the logics of the digital system, (relying more and more on quantified popularity metrics instead of critical evidence, for instance), or will the system react more unpredictably? Second, we require a set of interactions to interpret. As we will discuss in the next chapter, that means that we will need to collect hypertext (and the encoded metadata attached to hypertext). Third, we will need a way to predict and to test the influence of interactions. The final chapter is dedicated to this task but recall that we already have an outline of the process from our principles of system analysis. The analysis will have to be iterative and it will have to be self-reflective, because systems can only be understood in terms of their becoming in time.

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Notes 1. Assuming, of course, that we are not blinding ourselves to repeat events through the way that we choose to look for them—that is, assuming our analytical stance or our measuring instruments are not systematically biased. 2. I am not suggesting, here, that a networked-politics style approach, in which digital connections between actors are somehow realised in material politics, is illogical. My point is that networking logics cannot be transposed on to material relationships directly (and thus be used to explain material actions) because the ‘communicative distance’ between these two systems is far greater than is easily acknowledged—and certainly far greater than between the communicative logics of the respective systems. There is enormous internal differentiation required to present a network to a political public, and then considerable interpretation required to translate that interaction-­ set into political action. As such, there are significant assumptions in many networked-politics models that are rarely acknowledged. 3. There is so much rich and insightful literature engaging with the politics of class, gender, ethnicity, labour, marginalisation and exclusion in digital media, I do not know how to acknowledge it properly here. Suffice to say that it’s easy enough to find, and largely valid and urgent reading for anyone interested in the ongoing political differentiation of the internet. 4. While de Saussure imagined a universal system for the formation, organisation and operation of language, its manifestation was culturally specific: ‘The culture and its needs determine the categories of meaning’ (Lewis, 2008, p. 112). The langue, to use de Saussure’s term, ‘is inevitably bound to the social and cultural context in which the language parole (specific utterance) is operating’ (113). In his later work, Wittgenstein wrote of language games referring to the interplay of imprecise meanings that can attach to signs within a semantic system. (Wittgenstein, 1922) 5. In the interest of accuracy, it should be noted that current Prime Minister Scott Morrison was still treasurer in 2017, when he bought a lump of coal to question time in the House of Representatives. Liberal leaders are very much assemblages themselves, always becoming (or going)—as Deleuze noted, ‘these are not successions, lines of descent, but contagions, epidemics, the wind’. 6. This is not a political science book, so it is largely beyond its scope to consider the many ways in which the business of politics continues away from television screen and Twitter feeds. Power, extraction and violence are real enough and continue everywhere. It’s important to recognise that truth and to avoid Baudrillard’s more extreme provocations: wars definitely take place, and if you happen to be in such a place, then I’m quite sure that the idea of performative politics must seem appalling.

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7. This kind of ‘two-worlds’ analysis recalls a dualism critique that has long been levelled at digital technology, an idea that the digital and physical are indeed somehow separate, and that this separation is problematic for earth-­ bound humans (e.g. Jurgenson, 2012; Turkle, 2011). I do not intend to recall this debate in any detail, but it is interesting for how it reflected our uncertainty about how best to reconcile an emergent digital logic that seemed divergent from the established order. How could these systems exist alongside one another when they ‘felt’ so discontinuous? How would our online actions translate into the physical world? What was a Facebook friendship worth?

References Altheide, D. A., & Snow, R. P. (1979). Media logic. Beverly Hills, CA: Sage. Anderson, B. (1991). Imagined communities: Reflections on the origin and spread of nationalism. London/New York: Verso. Bamford, J. (2004). A pretext for war: 9/11, Iraq, and the abuse of America’s intelligence agencies. New York: Random House. Barber, B. R. (2006). How democratic are the new telecommunication technologies? Paper presented at the Second Conference on the Internet, law and politics: Analysis and prospective study, Barcelona, Open University of Catalonia (UOC). Barthes, R. (1988). The semiotic challenge (R.  Howard, Trans.). New  York: Hill & Wang. Belakova, N., Goodman, E., Rahali, M., Speller, C., Taylor, R., & Ziemer, J. (2018). Tackling the Information Crisis: A Policy Framework for Media System. The London School of Economics Truth, Trust and Technology Commission. Retrieved from http://www.lse.ac.uk/law/news/2018/truth-trust-technology Benkler, Y. (2006). The wealth of networks: How social production transforms markets and freedom. Connecticut: Yale University Press. Buchstein, H. (2002). Bytes that bite: The Internet and deliberative democracy. Constellations, 4(2), 248–263. Burck, J., Hagen, U., Höhne, N., Nascimento, L., & Bals, C. (2019). Climate change performance index: Results 2020. Retrieved from Berlin, Germany. Castells, M. (2009). Communication power. Oxford: Oxford University Press. Couldry, N., & Hepp, A. (2013). Conceptualizing mediatization: Contexts, traditions, arguments. Communication Theory, 23(3), 191–202. https://doi. org/10.1111/comt.12019. Davies, N. (2008). Flat earth news: An award-winning reporter exposes falsehood, distortion and propaganda in the global media. London: Chatto & Windus. Dean, J. (2001). Cybersalons and civil society: Rethinking the public sphere in transnational technoculture. Public Culture, 13(2), 243–265.

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Fuchs, C., & Mosco, V. (2016). Marx and the political economy of the media (Vol. 79). Leiden, Netherlands: Brill. Gillmore, D. (2004). We the media: Grassroots journalism by the people, for the people. Sebastopol, CA: O’Reilly Media. Habermas, J. (1984). The theory of communicative action vol. 1: Reason and the rationalization of society. Boston: Beacon Press. Habermas, J. (1991). The structural transformation of the public sphere. Cambridge, MA: MIT Press. Habermas, J. (1994). Three normative models of democracy. Constellations, 1(1), 1–10. Hall, S. (1980). Coding and encoding in the television discourse. In S.  Hall, D. Hobson, A. Lowe, & P. Willis (Eds.), Culture, media, language: Working papers in cultural studies (pp. 197–208). London: Hutchinson. Hassan, R. (2018). There isn’t an app for that: Analogue and digital politics in the age of platform capitalism. Media Theory, 2(2), 1–28. Howard, P.  N., Duffy, A., Freelon, D., Hussain, M.  M., Mari, W., & Mazaid, M. (2011). Opening closed regimes what was the role of social media during the Arab Spring? Retrieved from www.pITPI.org Jacobson, T. L., & Pan, L. (2008). Indicating citizen voice: Communicative action measures for media development. Paper presented at the Annual Conference of the International Communication Association, Global Communication and Social Change Division, Montreal, Canada. Jacobson, T. L., & Storey, J. D. (2004). Development communication and participation: Applying Habermas to a case study of population programs in Nepal. Communication Theory, 14(2), 99–121. Jurgenson, N. (2012). When atoms meet bits: Social media, the mobile web and augmented revolution. Future Internet, 4, 83–91. https://doi.org/10.3390/ fi4010083. Kessler, G., Rizzo, S., & Kelly, M. (2019, October 14). President Trump has made 13,435 false or misleading claims over 993 days, Online. The Washington Post. Retrieved from https://www.washingtonpost.com/politics/2019/10/14/ president-trump-has-made-false-or-misleading-claims-over-days/ Latour, B. (2004). On using ANT for studying information systems: A (somewhat) Socratic dialogue. In C.  Avgerou, C.  Ciborra, & F.  Land (Eds.), The social study of information and communication study. Oxford, UK: Oxford University Press. Lewis, J. (2005). Language wars: The role of media and culture in global terror and political violence. London: Pluto Press. Lewis, J. (2008). Cultural studies –The basics. London: Sage.

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Lewis, J. (2015). Media, culture and human violence: From savage lovers to violent complexity. In London. New York: Rowman & Littlefield. Love, N.  S. (1989). Foucault & Habermas on discourse & democracy. Polity, 22(2), 269–293. Moffitt, B., & Tormey, S. (2013). Rethinking populism: Politics, Mediatisation and political style. Political Studies, 62(2), 381–397. https://doi. org/10.1111/1467-9248.12032. Musgrove, M. (2009, June 17, Wednesday). Twitter is a player in Iran’s drama. The Washington Post. Retrieved from http://www.washingtonpost.com/wpdyn/content/article/2009/06/16/AR2009061603391.html Patterson, T. E. (1993). Out of order: An incisive and boldly original critique of the news media’s domination of America’s political process. New  York: First Vintage Books. Pond, P. (2015). Questions, time, and tweets: Exploring network temporality and social media engagement with televised political debate. Television & New Media, 17(2), 142–158. Pond, P. (2016). Software and the struggle to signify: Theories, tools and techniques for reading Twitter-enabled communication during the 2011 UK Riots (Doctor of philosophy). Melbourne, Australia: RMIT University. Remeikis, A. (2019, December 12). Morrison responds to fears over bushfires but rejects censure of climate policy, Online. The Guardian. Retrieved from h t t p s : / / w w w. t h e g u a r d i a n . c o m / a u s t r a l i a - n e w s / 2 0 1 9 / d e c / 1 2 / morrison-responds-to-fears-over-bushfires-but-rejects-censure-of-climate-policy Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier. Cambridge, MA: MIT Press. Roth, D. (2017). The president of blank sucking nullity. Retrieved from https:// thebaffler.com/latest/the-president-of-blank-sucking-nullity-roth Samuels, R. (2009). New media, cultural studies, and critical theory after postmodernism: Automodernity from Zizek to Laclau. London: Palgrave Macmillan. Shirky, C. (2011). The political power of social media: Technology, the public sphere, and political change. Foreign Affairs, 90(1), 28–41. Stahl, T. (2013). Habermas and the project of immanent critique. Constellations, 20(4), 533–552. https://doi.org/10.1111/1467-8675.12057. Starbird, K., & Palen, L. (2012). (How) will the revolution be retweeted? Information diffusion and the 2011 Egyptian uprising. Paper presented at the CHI 2010: Crisis Informatics, Atlanta, GA, USA. Thompson, J. B. (1991). Ideology and modern culture: Critical social theory in the era of mass communication. Stanford, CA: Stanford University Press.

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Turkle, S. (1996, January). Who am we? Wired, 4(1). Retrieved from http:// archive.wired.com/wired/archive/4.01/turkle.html Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. New York: Basic Books. Turner, G. (1996). British cultural studies: An introduction (2nd ed.). London: Routledge. Wittgenstein, L. (1922). Tractatus Logico-Philosophicus (C.  Ogden, Trans.). London: Routledge and Kegan Paul.

CHAPTER 8

Hypertext Reality

The digital and the political systems differentiate through logical operations that I have now spent some time describing. My focus in remaining chapters is to consider how these logics influence interactions between the two systems and to answer specific questions about the ontology and epistemology of those interactions. Where do they take place? Do they take material forms? Can we observe them happening or take representative measurements? How does logical influence over interactions reveal itself? How should this influence be studied? The list of questions can grow very long indeed and recall that we are only considering a narrow set of interaction events here at the interface between just two system types. However, as I shall attempt to show, these specific examples share certain characteristics with all other interactions, because certain dynamics of communication and interpretation are universal. In Chap. 6, I attempted to record a history of significant events in the development of modern digital media technologies. I argued that software continues to be shaped by foundational logics that were inscribed in the original ‘discreet’ computing machines and in the networking protocols that eventually allowed those machines to communicate with one another. In Chap. 7, I sketched a summary of a broad and complex argument between logical alternatives for communicative politics: an idealised or a normative deliberation and a violent struggle to signify. I described how these different ideas had been developed alongside a brief record of internet-­ enabled political communication. The historical analysis of © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_8

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systems serves two purposes. First, it ensures that contemporary research engages with the full complexity of these systems, as it is already understood and documented. Second, it can reveal logical precedents, like networking and automation, which can inform our analysis of the current autopoietic process. If the analysis is conducted well, it should provide information about the current logical operation of the respective systems—logics that will afford some interactive possibilities and exclude others. A digital system is understood to be an assemblage of various sub-­ systems, and the retelling of that assemblage informs our understanding of the system. The digital systems we are interested in are complex. They produce meaning in abstract flows of text and images, GIFS and memes, through programmable recommendation and promotion engines and through layered webs of interconnected humans and media. Specifying a focus, locating a dynamic of interest and influence, is essential for making these systems manageable and intelligible. We can always expand our knowledge, as long as we our building on solid foundations, but if our initial observations are uncertain, if we are too reliant on improbable assumptions, then these are issues that can never be corrected. I am fairly convinced that the foundational logics of digital systems, logics of automation and networking, which scale into logics of programmability and datafication, connection and popularity, have a similar on shaping potential interactions despite minor variations in logical sense making (internal sign-systems or symbol codes). That means that the logics work the same way for lifestyle images on Instagram as they do for political tweets on Twitter and monetary pledges on Kickstarter. The logics of our political systems are more variable and weaker—I am less convinced that they shape interactions quite so predictably. The various norms, precedents, codes and institutions through which liberal principles of deliberative democracy are prescribed are subject to many more types of internal disruption (and, of course, disruption from anti-­ democratic systems). There is great variability in the interpretation and application of governmental logics at all levels of the democratic process: I think it is far harder to say that the political system in France shapes for digital interaction in the same way as the system in Thailand, or Port Phillip council in Melbourne, Victoria. Obviously, the Chinese case offers the most disruptive data here, but then the Chinese political system is so far removed from the French, in terms of its logical operations, it confuses the purpose of any comparison. If the previous chapter did not make it

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sufficiently clear, we are focussed on a narrower logical set here, and it is pretty much the logics of western liberalism. So what happens objectively when the digital system and the political system interact with each other? What are the material and symbolic processes that accompany the introduction of a new media tool into an active political public, for instance? As stated, there are some obvious questions that we should be asking to help us towards an empirical analysis of these events. First, we need to know where these interactions are taking place— or more precisely, we need to locate the interactions that we believe are likely to be especially influential in terms of political differentiation. Of course, there are multiple possibilities here, an almost infinite number of potentially influential points of contact in the interaction field, but our review of the respective system-logics has identified for us an event-set that should be especially influential given the context of study. Second, we need to address the material (or otherwise observable) phenomena that correspond to this event-set. We are seeking to describe digital-political becoming in time, space and human knowledge. Third and finally there are a set of questions that should prompt us towards an analytical strategy for interpreting the significance of discreet interactions within the wider field of systemic becoming.

Where Do Systems Interact? When systems interact, meaning production always involves some sort of value assignment within a wider set of symbolic codes. Systems produce meaning for themselves; when we observe systems interacting, the meaning produced is a product of their respective communicative processes, through which they assemble internal-facing meanings via a shared set of signs and codes. We then interpret that shared set for ourselves, further differentiating meaning through our own internal-facing logics. Recall that this was a key point for discussion in Chap. 4, when we considered the social systems theory of Niklas Luhmann and asked whether it was possible to compare communication acts to interaction events. I hinted that I thought that this comparison could indeed be valid, and I have subsequently been advancing the case that communication and interaction are largely aligned. All systems, when they interact with other systems, do so through some sort of intermediary communicative exchange, either material or symbolic or both, that produces information which both systems must then

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interpret internally in order to reproduce themselves meaningfully. Interactions are also signification events. We are trying to observe systems in the process of making meaning for themselves and then interpret that meaning for ourselves. As observers, we can collect that encoded information, we can interpret it according to our own logical precedents and we can interpret it on behalf of the participating systems, as long as we have some understanding of their internal differentiation logics. That means, no matter what initial system we are trying to observe, and regardless of the wider system-set, we begin our observation by focussing on the interactions that engage the system in acts of signification. In this way, assembling processes are made available to us as objective reality, though always conditioned by our observing perspective. So, first, we must answer the question: where do the digital and political systems interact in ways that are most meaningful? We can use the systems that were meant to be interested in here: the social networks and news channels through which we create, share and contest political discourse. In fact, these systems are ideal because it is the discourse itself that we want to study.1 When a digital media system (like Twitter) interacts with another social system (like a political party or an ideological group) both systems produce meaning through their respective interpretations of discourse, the flow of images and text, in which multiple meanings are encoded. From the human perspective this is obvious: tweets are text; we use hashtags as shorthand for our activism while entire political strategies (somewhat depressingly) reduce to catchphrases: ‘leave means leave’ or ‘witch-hunt!’ Less obviously, perhaps, the machines work with text too. Twitter algorithms ‘read’ hashtags, judge sentiment and parse phrases; JSON—Twitter’s data storage language—is a massive dictionary of dictionaries in which variables are assigned value through text. Automation demands abstraction and quantification, so the text may look different, but it is still text, still a product of the same symbolic system. Interactions are still hard to pin down, precisely because signification occurs in this uncertain ontological space, neither wholly within nor between systems. I have just called this ontological space ‘discourse’ and ‘text’ within the same paragraph—this seems like it might be a problem. In fact, there are two different complications here. In Chap. 5, I suggested that interactions between systems had to be interpreted probabilistically, because a single observation of a system-system configuration can only be an approximation of the system’s interactive potential. As a result, where (and how) we look for interaction will influence the interactions we

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observe. The second complication arises from communication itself and it is this issue of internalised meaning-making. The tripartite communication model situates interpretation (meaning-making) within systems, even when information is being exchanged between systems. Consequently, even if (and when) we can observe informational exchange independently, our evaluation of how (and why) that information is meaningful remains limited. As such, it is not only our observation-act that is probabilistic, but also the act of situated meaning-making itself. Meanings float in probabilistic clouds between systems, only becoming fixed when a specific system takes a specific perspective on the cloud (an event), thus assigning attention for a particular time to a particular array of sign-signified relations. Interactions always take place between the cloud and a situated system, rather than directly between systems. This uncertain, intermediary space is what we need to observe: it is the complete and encompassing sign-system, all the possible connotations and denotations between signifier and signified. In theoretical terms, specific to digital-politics, we can call this discourse and assume that through discourse actions are made meaningful. In material terms, we access discourse through ‘text’—the different technologies of language, writing and symbolic representation that humans have invented to share meanings with each other. An interaction, then, is an event in text that redirects system-attention, reordering signs into interpretable informational structures. Systems decode these structures according to logical processes that themselves are also developing through these self-referential loops. This formulation may sound familiar to cultural theorists but also problematic. Historically, this type of theorising has a tendency to lose itself in postmodern loops of endless deferral. The interaction event disappears from any shared sense of objective happening and empirics becomes impossible. Words have no ‘origin’ as Derrida famously proclaimed (Derrida, 1967). However, system interactionism is striving for empirical or experimental access to systemic complexity and postulates conceptual and methodological resources that transcend human meaning-making. A fundamental tenet of system interactionism is that systems are both human and non-human, material and symbolic.2 My challenge in the remainder of this chapter is to apply interactionist empirics to our critical knowledge of the semiotic struggle. Not all theorists will agree with the assertion that digital politics is fundamentally textual. Some will argue that these are physical or embodied events that are (if anything) more real than anything in discourse, or that

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power relations or structural forces are more important for analysing the interplay between systems. I am not denying the validity of any of these ideas and I fully accept that there are other ways to frame interactions between systems. My argument is that complexity is only ever fully revealed by intimate engagement with the assembling dynamics of these higher-­ order social phenomena. There is a ‘level’ on which we require a theory of power relations to interpret political action, but that sort of analysis takes place on an entirely different scale from system interactionism. One of the key lessons from quantum mechanics is that phenomena can look surprisingly different at the granular scale and complexity can reveal itself unexpectedly. Ultimately, I would argue that the different approaches are fully compatible with each other. In fact, I would go further and suggest that a fully realised theory of power relations requires a theory of event-based signification, establishing the logical systemic precedents through which power is made and enacted. We will be looking for interactions within text that is shared between systems. This locates us firmly in the realm of communicative politics, and a discussion of normative models and Habermasian ideals, and it aligns with data structures and differentiation codes through which meaning is made within the digital system. Clearly, we cannot simply look at programming/code and ask if it looks deliberative, and we cannot read political discourse and ask: does this look programmed? Instead, we must observe textual ‘events’ and analyse them critically from the assumed perspective of the participating systems. In essence, we are asking: given this observed event, and given our knowledge of the differentiating logics of the respective systems, how do we think each system will interpret this event and respond to it? Following this analysis, we can then ask: which logical set will exert more influence on subsequent interactions in the temporal order?

Hypertext What sort of text are we studying here? There is already a great deal of literature concerning this subject, including the affordance theories discussed earlier, as well as critical textual approaches, digital literacy studies and so on. In all of these fields, there are ideas about what should ideally be read in any analysis of a technology, ideas about how the technology presents as text. System interactionism doesn’t preclude these alternative approaches, but its emphasis on signification does radically simplify the

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process of analysis: what’s read are symbols in the meaning-making cloud—the text is text. As I argued earlier, computer code utilises the same symbolic logics as human language, and frequently it uses the same set of signs when it assigns values to variables, strings are often human words, there are integers and decimal numbers, Boolean and binary choices. Furthermore, the logics of connectivity and programmability, popularity and datafication are deeply inscribed into that code, and so there is a very real sense in which the autopoietic logics of the system act directly on the textual domain.3 Advances in natural language programming have further aligned the logical interpretation processes of the digital and political systems. Computers are increasingly trained to recognise the same textual indicators that human readers do and to fashion meaning from those indicators in ways that ape human systems. Indeed, I would suggest that we are approaching the point with digital-politics when we should begin to question Luhmann’s claim that separate systems are mutually unintelligible. There is a point when two separate systems are consumed by larger functional logics, when the analytical frame necessarily widens in scope, and we begin to talk in terms of component sub-systems rather than independent entities. Whether we are there already or not, the point is that the two systems here, the digital and the political, both make meaning from the same set of textual signs, and their logical codes for doing so are convergent. Indeed, we can see the mutuality already in the world in the form of hypertext, a language designed and developed to translate between these systemic logics (e.g. Manovich, 2003). Hypertext is a thing, a system in its own right, because language is also a thing (Ritzer, 2012, p. 78)—it has an epistemologically objective existence despite the fact that human beliefs and attitudes are integral parts of that existence. Both digital and political systems now produce meanings as hypertext. Global media is hypertextual. In many practical aspects, this is what the meaning-making cloud has become: dense flows of hypertext surging through networked tubes, wrapped around the globe like a dense, fibrous web of capillaries. Meanings are negotiated in this space, which is mutually accessible to both systems. We can observe this negotiation happening in time because the hypertext itself changes—literally, words change, hyperlinks move and meanings defer through differentiation pathways. The configuration of signs changes. Attention is redirected within the meaning-­ making space. Previously, I have likened this dynamic to a spotlight

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sweeping over a stage and settling on an actor, the audience’s attention now singularly focussed on her words. Hypertext is where systems struggle for control over the spotlight. The study of hypertext is a vast field in its own right and many readers will have considerable familiarity with both the hypertextual concept and its practice. For many of us, the idea of hypertext is inextricably bound with the Web, and in Chap. 6 I discussed briefly the significance of Tim Berners-Lee’s hypertext mark-up language and the transfer protocol that organised documents through the internet’s connective architecture. These inventions were critical for the popularisation and global adoption of the internet. The idea of hypertext, however, and the logical principles by which it is organised, predate the web by many decades. Hypertext is a peculiar and particular instantiation of text and if we want to understand how different systems interact through it, then we must become familiar with these peculiarities. It’s possible to give a very simple definition for hypertext. For instance, the World Wide Web Consortium (W3C), the international organisation that advocates for web standards, calls it ‘text which contains links to other texts’. However, as the consortium’s dictionary entry also suggests, the implications of this simple mechanical definition are more complicated. In many ways, the idea of hypertext is more powerful than the actuality, because of what it promises for the practices of text itself. Linking can be transformative: it realises ‘text which is not constrained to be linear’ (W3C, 2019). Obviously, we need to engage with the mechanical technology of hypertext for empirical study, but if we want to appreciate fully the implications of this non-linearity, then we also need to spend time with the idea, both as it was first realised and as it has evolved since. Describing hypertext can sometimes feel like a technological pursuit and sometimes like a transcendent literary one. The pioneers of hypertextual thinking tended be computer scientists or technologists, but often they were thinking about profound cultural or creative questions—a consequence, perhaps, of surviving the Second World War and working with the technologies of mass social destruction. In some sense, hypertext began as an imaginary of a better way of being, predicated on the same sort of informational organisation-liberation arguments that we identified among the early internet evangelists. This ideal is clearly evident in Vannevar Bush’s ‘memex’ concept, ‘a device in which an individual stores all his books, records and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility’ (Bush,

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1945). It is a profound enlightenment ideal, of course, liberation through knowledge, made technologically possible by a super-powered information organisation and retrieval system. Man is empowered to be always operating with all his available knowledge. Doubtless, there is a strong sense of clever men reimaging the species in their own image. The idea of hypertext continued to resonate with computer developers and clearly influenced the inventors of Advanced Research Projects Agency Network (ARPANET) and its early adopters, but the word itself was coined by Ted Nelson in the 1960s to describe his own information-organisation invention. Nelson conceived both a different way of storing information and a different way of accessing it. His arguments are too involved and complex for me to review here, but it should be noted that Nelson conceived text as a temporal phenomenon—something that could most efficiently and effectively be described (and stored) through its changes in time. His idea that text is something like an ongoing braid, to be wound and unwound in time, led him to suggest a ‘library’ model that tracked and stored changes in textual fragments—individual units of becoming. While the user of a customary editing or word processing system may scroll through an individual document, the user of this system may scroll in time as well as in space, watching the changes in a given passage as the system enacts its successive modifications. (Nelson, 1981, p. 2/18)

Nelson imagined a connected informational system with capabilities far exceeding those of the modern web, which being literary he called Xanadu. It’s a storage model that allows for branching within text, as when a single braid is divided into alternate strands, and for all those alternatives to exist simultaneously alongside each other. Within his temporalised storage structure, a hypertextual link could be made between any of emerging document-fragments: Xanadu is the archetypal dream of a hypermedia network. … Everyone will have the ability to produce their own documents and connect them to other public documents. The author may constantly create new versions of her own documents that include reference elements of many documents by many authors; public connections made between one version of the document and another version of another will usually automatically place themselves in the extant versions. Historical backtrack and degradation-proof storage will us to visit any version, any moment in the network’s history. Xanadu is the ultimate archive—with each element of this archive, however,

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constantly in process; viewed in hierarchies, and yet with alternative hierarchies always available; with the back end (knowledge structure) and the front end (depiction for the current display device) abstracted and split apart so that each user may have her preferred view of the information. (Wardrip-­ Fruin & Montfort, 2003, p. 441)

The overlap with assemblage theory and the systems idea of an interaction field is self-evident. Hypertext reimagines the document as record and so allows new possibilities for knowing and being through text. It is these possibilities (and the probabilities that they become) that inform our study of system interaction through hypertext. However, the material technology of hypertext, at least in so far as it is realised through the Web, is significantly more prosaic and less expansive than the concept of Xanadu. Web users do not have the ability to create or to link or to reorder text or indeed to explore the network of hyperlinks with the sort of freedom or the agency that Ted Nelson envisioned. Nevertheless, within the hypertext that the web has delivered, there are echoes of those initial imaginaries.

Hypertextual Logic So what can modern hypertext do? What are the logical precedents that it delivers? As most users of the Web will know already, hypertext remains pretty much as it was when first deployed by Tim Berners-Lee. Nelson’s idea that users would be able to create and insert hyperlinks into text for themselves as they read, and through this practice extend the document with their own meanings, has not yet been fully implemented. To an extent, then, the authority of the author survives, at least materially, and at least over the connections that locate the document within the network. What has happened is that linking practices have been extended to include other forms of media. In 2019 the Web is a library of fully interconnected multimedia forms. The average social media feed will feature text and images and video and GIFs, so although it is not the revolutionary creative space that was once envisioned, it is an incredibly dense and immersive mediascape:4 an intense meaning-making environment. The modern web is an ‘exploratory’ rather than a ‘constructive’ hypertext to borrow two terms from Michael Joyce (2003). It affords to its users non-linear, discursive and disruptive ways of reading, but not necessarily the same ways of writing. The tension in this idea reflects the tension of authorial authority-instability that the deconstructionists exposed, but

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it also explains some of the evident power-politics that we see in modern digital media. For instance, the Clinton administration’s decision to privatise the network in the mid-1990s largely transferred control of authorship to corporations. On many platforms, then, while citizen users are able to both read and write, the corporation retains ultimate control over the library-system and is thus able to organise hypertext in ways that it finds most profitable. A perfect example of this was provided by Facebook founder Mark Zuckerberg, who argued incoherently before Congress that, while Facebook would remove user access to organic political content if it were ‘fake’, its commitment to First Amendment principles meant that it would place no such restrictions on paid political advertising (Kafka, 2019). This imbalance between exploration and construction in the global hypertext system has two logical effects. First, the more discursive or dissociated meaning-making practices are largely hidden from surface-­ readings of the web and, second, hypertext increasingly operates in accordance with the priorities of its corporate-political owners. We can consider both of these effects if we return to the technological features of hypertext and reflect on how those features contribute to meaning-­making practices. Hypertext is an information storage structure that supports linking between documents or between resources within those documents. The concept is simple enough to grasp, even if one is unfamiliar with code or hypertext mark-up language (HTML). In practice, hyperlinking involves bracketing a resource with linking ‘tags’ or anchors and then specifying within those anchors a destination to which the link should point. A resource can be anything within a document: a whole paragraph or a single character within a word, an image or even a blank space, as long as the dimensions of the blank space are specified within the document model. Hyperlinking is now so embedded in our media practices that pausing to explain it in this way feels redundant and condescending. We all know what hyperlinking is and what hyperlinks do because we spend our days using them. Our interaction with the internet happens through hyperlinks. Whenever we like a social media post, read an article or open a message, we are working with hyperlinks. Every click and every tap online requires a hyperlink to translate it into action. If we pause for a moment, though, hopefully we can see that this everyday practice has some remarkable implications for how we use text to construct knowledge. To illustrate this point, I’d ask you to consider an

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incredibly simple and common use of the hyperlink. Imagine that you are reading an article about ‘apartheid’ and the first use of the word has a link anchored to it: the link directs an external resource—Wikipedia, for instance—which appears to add a definition and context to the word. Now, communication studies has already established that none of our words have meaning that is fixed or absolute. Meaning is established relationally, within a wider system of signifier-signified arrangements, and meaning is both denotational and connotational, because senders and receivers interpret words differently. So we accept that the meanings of words are always a little unstable and that sometimes it suits us to interpret a sentence in a particular way, even if we suspect that is not what was intended. In effect, then, though the text is public, we read and write mostly in private. What happens to the signifier-signified relationship when a contested word is hyperlinked to more text? It’s possible, of course, that the additional text adds definitional and contextual meaning to help the writer assert more control over what she ‘meant’ when she chose the word. In this scenario, perhaps we can think of the link like it were an explanatory footnote—a lawyerly contribution that restricts the potential meaning of the word to as narrow a range as possible. This, however, would be a rare case. More likely is that the hyperlink dramatically expands or confuses the possible meaning of the word. Why is this the case? Well, first, because the vast majority of the human population does not write as though they were lawyers and, second, because it is effectively impossible for the author to exert control over the wider hyperlinking network. What if there is another hyperlink in the contextual resource? Or what if the contextual resource is not owned by the original author, and it is frequently rewritten or updated? More foundationally, what if the reader does not click on the hyperlink or does click on it but then only reads the first sentence of the supporting resource. Of course, no author can ever control how her words are read, but hyperlinking makes even the impression of control seem ridiculous. We can say that hyperlinking destabilises the word: it can ‘explode’ the surface textuality of the word itself and it dramatically complicates the mapping of meanings on to signs. We can observe this happening quite easily: think of the tweet or the hashtag—two of the defining textual characteristics of the micro-blogging service Twitter. For most of its existence, Twitter limited tweets to 140 characters in length, which clearly restricted how much meaning anyone could make through a single tweet. Users quickly found ways to extend this character limit, threading tweets

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together for instance, but still, Twitter was hardly a discursive way of writing: most of the time, whatever one had to say, it had to be said quickly. What hyperlinking does, however, is destroy the notion that a tweet is a contained or unified object. When Twitter was still relatively young, still had its 140 character limit, and offered users far fewer options for embedded media objects or links into tweets, I conducted an analysis of large dataset and found that nearly two thirds of tweets already contained hyperlinks to external media objects stored elsewhere on the web. At the time, I reflected on what it meant for meaning-making if a tweet contained a link to a single image: the act of user-interaction with the tweet is complicated by the link to the image. Temporality will depend upon whether or not the link is clicked, or if the image is viewed, or if the tweet contains commentary that invites reflection or feedback. In short, much like any sign, the tweet resides within a system of meaning and temporal deferrals. The stubbornness of the system to defy categorisation is an open question: certainly its complexity is resistant. (Pond, 2016, p. 269)

It is deferral that so destabilises meaning-making. Not only does the interpretative ‘scope’ of the word expand exponentially, that expansion is largely hidden.5 There are an incalculable number of different pathways that a reader can explore: ‘embedded links create windows into a vast web of interactive text, the limits of which can only be guessed at’ (p. 346). Clearly this uncertainty complicates any attempt to locate hypertext objectively at the centre of the meaning-making dynamic. Production is largely visible but reception is mysterious—we cannot know how a reader interacts with its multiplicities. Indeed, that process is largely hidden even from the subject—only the corporate owners have full access to the click trails, hover times and scrolling rates that fully describe how the audience reads the hypertext web. Signification through hypertext is paradoxical. While it is certainly true that hypertext defers and hides meaning-making it is also the case that collectively we have never known more, nor collected more data, on the secrets and the private behaviours of the interpretative subject. The data and the advertising economies create these imbalances, of course. Social media companies achieve multi-billion dollar valuations precisely because they can capture the receptions and interactions of their users and sell this information rather than give it away. The structural biases and power relationships that this ‘surveillance capitalism’ creates are

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well enough known by now (Zuboff, 2019), though recognition of the problem has done little to stall it happening or to avert the consequences. The implications for human phenomenology are less clear. As well as deferral, hypertext is dissociative. In recent years, the idea that we increasingly inhabit ‘filter bubbles’ (Pariser, 2011) has gained considerable traction, especially among political commentators, many of whom worry that these atomised and isolated mediaspheres fatally undermine a shared sense of political and social morality.6 The sense of dissociation is identifiable in debates about differences between the online and offline ‘worlds’ (Carr, 2013; Hassan, 2019; Jurgenson, 2012; Turkle, 2011). These issues need not delay us overmuch but, personally, I think that the persistence of these debates speaks to the dissociative uncertainty that internet users feel in hypertext. When we are online, we largely act through hypertext—that is, through our clicks and our scrolls, through downloading media and uploading data to the corporate panopticon. We forget ourselves in these repetitive interactions, lose track of where we have been and where we thought we were going. Meaning can feel disembodied, maybe even somewhat unreal, and that of course has implications for the values (moral, economic, social) that we assign to our (inter)actions.

Hypertext and Capital Significant public discourse now happens in proprietary corporate spaces. It’s part of the same trend that has turned once public venues in towns and cities into private retail malls. Hypertext flows in these spaces, but it flows in pre-programmed channels and according to currents that may be hidden and may be impossible for sender and receiver to understand. Lev Manovich has written about the logics of these spaces and described how new media becomes digital data controlled by software (Manovich, 2003). In his terms, deferral and dissociation are captured by the infinite variability possible in hypertext: ‘commercial Web sites programmed to customize Web pages for each user’ (p. 17). Additionally, control is exerted by cultural algorithms operating on data that must be modular and numerical—certain textual forms submit more easily to manipulation than others. The language of our discourse is shaped by these commercial imperatives and perhaps our politics becomes programmable for profit. To illustrate this we can return, for a moment, to this issue of fake news on Facebook. There’s no need to get deep into the details of how fake

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news was spreading on Facebook or whether or not the company was amplifying this effect through its commercial decisions. What’s relevant, here, is that the company responded by formalising a series of changes to the programmability logics governing the operation of the news feed. On one level, changes of this type happen every minute of every day—platform capitalism relies on hyper fast ‘self-programming’ machine learning techniques to game minute competitive advantages in the online advertising marketplace. On another level, though, these changes represented a higher-order shift in emphasis, as the company attempted to mitigate criticism in the political system by reordering logical priorities within its internal, programmable operations. Facebook’s issue was that the news feed functioned to extract maximum revenue from the online advertising companies that transact massive online ad-buys on behalf of clients. Advertisers have specific demands: they want to reach the maximum number of people as possible with every dollar that they spend, and they want the cohort that they do reach to be susceptible to their messaging. On Facebook, like much of the web, advertisements ‘piggy-back’ on other content, either because they are served on discreet webpages alongside this content or because they circulate within media feeds as ‘content’ themselves. In trying to price the content that its users created, so that it could sell advertising slots associated with that content, Facebook prioritised the advertisers’ demands: high value content would reach a lot of people and those people are closely aligned to the advertisers’ target markets. In the simplest possible terms, this meant that the news feed was engineered to reward content that performed particularly well against the logics of popularity and connectivity. In other words, Facebook made it easier to make money if you could produce content that was going to appeal reliably to large groups of similar people. It was this programmability decision that drove contentious consequences in the political system. For a start, though the algorithms themselves were clearly incredibly complex, making millions of micro-adjustments to how they served content and directed traffic within Facebook, the logical complexities were not. They were blunt and easily gameable. Content producers calculated that large audiences were susceptible to messages that produced heightened emotional responses without challenging established prejudices. In other words, Facebook was set up logically to reinforce practices that were already reliably popular and then to connect those practices between user groups. Fake news website spread widely in the right wing American

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mediasphere because that audience was already addicted to a cocktail of incendiary rhetoric and spurious accusation. During the 2012 election in the Baffler magazine, Rick Perlstein documented the various ways in which ‘conservative’ media had been monetising exactly this sort of content for years (Perlstein, 2012). In effect, Facebook encouraged users to pump content into this space because this was the type of content that they could reliably use to sell advertising. The changes to the logical priorities that Facebook has announced since the 2016 US presidential election supposedly reduce the emphasis placed on certain popularity measures by aligning Facebook content flows more closely with those elsewhere on the web. In effect, if a site appears disproportionately popular within the Facebook system, Facebook will assume that its system is being gamed. Perhaps this will make ‘fake news’ strategies harder to implement, because large systems are more complex and therefore, presumably, harder to game, but it is not a long-lasting solution. Advertising dollars are influential everywhere online, and the rest of the major media platforms also direct traffic according to similar popularity logics. So, in effect, it does nothing to fix the underlying problem, which is that the commercial logics of Facebook align in unfortunate ways with the consumption logics of an agitated political public.

The Political Hashtag What happens when a digital system meets a political system? What are the material and symbolic processes that accompany the introduction of a new media tool into an active political public, for instance? The internal differentiation logics of the separate systems shape how those systems ‘present’ to each other. ‘Shape’ in this sense means that the logics will make some interactions more likely and other interactions less likely. For instance, the political public will promote some topics in discourse over others. A group of highly engaged Democratic activists is more likely to use Twitter to discuss the impeachment of Trump than it is to discuss astrophysics. An interaction is an event that redirects attention within this meaning-­ making space, reordering the signs into interpretable informational structures. Systems decode these structures according to logical processes that themselves are also developing through these self-referential loops. Over the course of three chapters, I have argued that the digital and political systems interact hypertextually.7 Events are instantiations of hypertext.

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The publication of a tweet is an event; so is a retweet or a reply; a suggestion in the newsfeed; the reassignment of a link or a reordering of the ‘trending topics’ list. An event is any reorganisation of temporal-spatial relations within the hypertextual sign-system. Relevant events are interpretable to both systems, influence both systems and must leave a material trace in hypertext. It is these material traces that we must try to observe happening.8 Every interaction happens under the tension of different systemic logics meeting. The tension is inherent to the interaction because each system strains to interpret the event in its own image, according to its own logics of differentiation. Every event in political hypertext, then, has a curious triadic structure, in which three different interpretative instantiations pull the probabilistic cloud away from its central pin. We strive to observe this rebalancing independently, weighing the influence of the different systems against each another, using prior knowledge and critical reasoning to reach logical conclusions. None of that is possible though unless we can first identify the events that matter—we need to know what we are looking for in hypertext. Acting as observers, how should we differentiate between those events that matter and those that do not? Somehow, we must locate interactions that are mutually meaningful to both digital and political systems. These will be hypertextual events that signify meaning to both systems through their respective encoding/ decoding operations; they must also have a material instantiation—an object form—that is accessible to an observer. In other words, the hypertextual change must be recorded somewhere, most likely in a database that a researcher can access. If an event does not leave an informational trace then, sadly, we have no way to consider it. How should we approach this task? First, we must establish how the interacting systems themselves produce meaning through hypertext. This is the primary reason why a critical historical reading of assembling logics is so important. We can note that the digital system, functioning through automation and networking, responding to neoliberal market-logics, abstracts political discourse, quantifies meaning into (representationally limited) data structures, which it submits to code that rewards popularity. The political system commoditises complex social and moral values, turning them into symbolic tokens, which it trades for votes with specific groups of the electorate. These publics argue over these dissociated symbols in a saturated media environment, in which audience attention is the dominant currency.

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These broad-brush strokes can frame our empirical work. The tendency of the digital system to abstract, quantify and sell political discourse to advertisers directs our attention towards interaction events that are likely to influence the political system. At the same time, we operate knowing that the set of interactions between the digital system and hypertext is exponentially larger than subset of interactions that also engages the political system. Therefore, we can use political language to delineate the contextual boundaries of our work. This is particularly true when we are interested in the political influence of digital technologies. We contextualise our research as soon as we state that we are interested in a specific type of political meaning-making in a specific political public. Event identification then involves searching for hypertext that encodes specific political signifiers: in the crudest sense, this may reduce to keyword searching or hashtag scraping. The logics of hypertext, politics and digital technology form a semiotic triangle, a confluence of interactions, through which political meanings take shape. So that we can explore this action, let’s focus for a moment on the political hashtag, a simple but ubiquitous hypertextual form on the modern web. Hashtags are common to every social media platform and tagging, in general, is a long-established syntax in the semantic ecology of the web. In blogging, a tag is a topical signifier, which authors add to posts to align that content with a wider catalogue of relevant documents. On media-sharing platforms, the tag is a way of sharing content with an interested community. On social discourse platforms, the tag or hashtag (literally a hashed-tag), signifies a contribution to a wider discussion. Bruns and Moe (2014, p. 17) argue that hashtags ‘help to coordinate the exchange of information relevant to … topics’ and signify ‘a wish to take part in a wider communicative process’. Twitter users employ the # symbol to align their tweets with particular topics. This use of the hashtag is a product of co-development between Twitter and its users, and often cited as an example of benevolent and accommodating adaptability on behalf of the company. However, Halavais (2014, p.  30) argues that Twitter ‘did more than merely make formal the informal workarounds of its users. These appropriations often displaced social practices that better represented the diversity of users and their needs, replacing them with model uses (and users) imagined by Twitter’s developers.’ The hashtag is Twitter’s macro communication layer. A keyword is attached to the # symbol (e.g. #MAGA, #Brexit, #MeToo) so as ‘to mark a tweet as being relevant to a specific topic and make it more easily

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discoverable to other users’ (Bruns & Moe, 2014, p. 17).9 In theory, this means that a tweet containing a hashtag has the potential to reach many more people than the direct followers of the publishing user. However, it should be noted that there is great variability in the way that users interact with the Twitter application: ‘the majority of users contribute to the Twittersphere via third-party applications’ (Halavais, 2014, p. 31). It cannot be assumed that users follow a hashtag in the same way that they follow individual users, nor that users interact with one hashtag in the same way as another. However, in certain circumstances, and for certain topics, the inclusion of a hashtag within a tweet may reflect the user’s intent to contribute to an ad hoc issue public (Bruns & Burgess, 2011). It should also be noted that it is very easy to query the Twitter API for hashtags. This may have contributed to the attention the hashtag receives from Twitter researchers. A huge amount of Twitter research is hashtag-centric, which has meant that the hashtag frames how the platform is understood and how its influence is assessed. Our aim here, though, is not to view the hashtag as a window into Twitter but to consider what the technological and sociological practice of hash-tagging does to the word being tagged. What happens when the political signifier (the word) is made hypertextual (the hashtag) according to the logics of a specific digital system (the platform)? In the final chapter of this book, I hope to demonstrate how we can use interactionist empirics to explore cause, effect and influence in the meeting of systems, but first it is worth describing what the outcome of these interactions looks like. Digital-politics is a phenomenon that reveals itself to us through the interplay of logical assemblage and it is helpful to describe that assemblage before we attempt to quantify the assembling dynamics. The first thing that happens when a political signifier becomes a hashtag is that it undergoes reduction. This reduction happens across domains of meaning-making. For instance, it reduces the complexity of political consciousness for the political author. Complex desires are necessarily diluted through ‘keyword’ representation, and these desires must then somehow be reimagined and reconstructed through attachment to the hashtag, which then acts like semiotic anchor, a leaden lump of certainty for floating fragments of uncertain prose. At the same time, interpretative complexity is reduced for the political receiver. Complex or nuanced arguments are reduced algorithmically to numerical competition between hash-­ tagged ‘trends’. Partly this is a function of semantic-compression and

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partly this happens because there is so much meaning being produced in the system. Political hashtags can receive thousands of contributions every second—an unreadable volume of text. Under these circumstances, reception necessarily becomes a mixture of stochastic sampling and black box machine-measurements. Following semantic reduction, automated processing transforms political meanings through numerical abstraction, so that the strength of an argument is now perceived in terms of tweets per minute or the popularity of an opinion is assigned a retweet value. Obviously, this is a reductive process too, but it is more than that—it is anti-deliberative, rewarding a type of popularity that runs counter to the deliberative logics of careful consideration and reflection. In the next chapter, we will explore this dynamic through an observation of interaction events. Reduction and numerical abstraction move the hashtag away from normative political meaning-making and towards competitive counting—a process that logically rewards the most popular contributors and those able to ‘game’ the logics of programmable data. This is the world of astroturfing and bots, ‘dark money’ advertising, psychometrics, sensationalism and controversy-seekers. It is also a world in which political power largely tracks the economic and social realities of the offline world. Research has shown time and again that user influence within the digital system is closely associated with user profile outside the system (both in terms of popularity and in terms of money). Obviously loud voices influence politics everywhere—this is not something specific to the digital system—but this influence is heightened through hypertext and the recursive programming of numerical popularity. In the graphic reproduced below, a single celebrity account contributes a single tweet to a hashtag that already has thousands of contributors. The contribution causes an enormous spike in the ‘ambient’ discourse—a measure of meaningful productivity—because the single celebrity account immediately engages so many potential receivers in the discourse, and network effects further expand this engagement exponentially. The graph below demonstrates the enormous influence that a tweet from an account with many followers can have on a measure of persistence, an adjusted visibility metric that is meant to capture the subtle dynamics of Twitter temporality (Fig. 8.1). Crucially, more hypertext does not necessarily produce more meaning. In simple terms, the idea that a hashtag can ‘coordinate’ discourse does not hold up to scrutiny, especially for high-frequency hashtags. There may

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Fig. 8.1  The graph demonstrates the enormous influence that a tweet from an account with many followers can have on a measure of persistence, an adjusted visibility metric that is meant to capture the subtle dynamics of Twitter temporality

even be a threshold, above which the density of interaction defies coordination, preventing discourse from ever ‘settling’ into normative productivity (Pond, 2016). It may be too simplistic to say that, under these circumstances, discourse is necessarily a violent struggle between political powers, but certainly there are fewer restraints against this happening. There is also some evidence that digital interaction ‘hollows out’ signification, creating a hyperreal politics full of simulacra, just as Baudrillard described happening. I began this book invoking hyperreality to help my description of the Covington affair and it’s relatively easy to point to examples of politics being made hyperreal through hypertext. I referred to some cases in the previous chapter. Trump and Brexit offer countless data points: at times the media performance seems to be all that matters. Obviously this is the case with Trump, for whom reality seems to have reduced to dark hours watching cable news, but it transcends Trump too. Once again he is a symptom rather than a cause. It feels like everywhere

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we are waiting for a reckoning with empirical truth but the reckoning does not come. Baudrillard would argue that the reckoning never comes because the performance is all that we have now—we have forgotten what was ever meant to be real or authenticate. Trump is the president who reveals that there is no president. I have made a case that there may be logical reasons for this—when mediatised politics interacts with an increasingly automated media machine, there are reasons why signification tends towards reductive, emotive and enraging populism. However, we are still painting these tendencies with broad brush strokes, when the intention was to plot carefully the complexity of the interactions. I have speculated why this hyperreal politics might be happening without investigating how exactly it comes into being. In the final chapter, I intend to address the practical and methodological challenges of studying how system signification happens interactively. In essence, this will involve applying the principles of an interactionist methodology to a problem of political signification. It won’t be possible to describe completely this methodological challenge, to resolve it, or to answer fully the political questions that I pose—work of that scope might require another book. Hopefully, though, in setting out the principles of interactionism and applying them to a clear and important question, it will be possible to demonstrate both the mechanics of the method and the need for them.

Notes 1. This presupposes the significance of these interactions in shaping social knowledge and hence rendering the material world meaningful (Lewis, 2005). 2. This is not such a different place from the one that the interactionists GH Mead and Percy Bridgman describe. Our access to the interaction field is always limited by our perspective: where we stand, which way we look and the limitations of our sight. Those limitations are the same regardless of the material form or symbolic complexity of the interaction itself. Observation always requires representation and interpretation, whether we are interpreting molecular collisions or political argument, but in the latter cases we are arguably dealing with higher degrees of complexity. 3. There is not space here to recount the many different ways in which logics act on code which in turn acts on text. In recent years there has been a huge amount of work done in Science and Technology Studies and Critical

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Software Studies on this subject—it has been a long while since anyone seriously advanced an argument that code was neutral or abstract in its operation, somehow separate from the humans who wrote it. Theorists have unpacked the multiple systemic influences on code—the gendered, economic and political biases that have an enormous insidious influence on the shape of our social media feeds. 4. I take this term from Arjun Appadurai. ‘What is most important about these mediascapes is that they provide (especially in their television, film and cassette forms) large and complex repertoires of images, narratives and “ethnoscapes” to viewers throughout the world, in which the world of commodities and the world of “news” and politics are profoundly mixed. What this means is that many audiences throughout the world experience the media themselves as a complicated and interconnected repertoire of print, celluloid, electronic screens and billboards. The lines between the “realistic” and the fictional landscapes they see are blurred, so that the further away these audiences are from the direct experiences of metropolitan life, the more likely they are to construct “imagined worlds” which are chimerical, aesthetic, even fantastic objects, particularly if assessed by the criteria of some other perspective, some other “imagined world”’ (Appadurai, 1991, p. 299). 5. Wikipedia’s frequent ‘edit wars’ are interesting case studies in this regard. The term refers to periods of conflict on the editorial pages of site entries, in which different users (or groups of users) contest definitions and aim to assert their own opinions. These struggles are largely hidden from the readers of the website but are infamous for their intense and often aggressive nature (Yasseri, Sumi, Rung, Kornai, & Kertesz, 2012). 6. Like many of the energetic ideas on the digital-political space, the appealing logic of the filter bubble may be more complicated and less compelling than initially thought (Bruns, 2019). 7. It’s important to note that this is not their sole interactive domain. Political systems produce privacy legislation that restricts what data can be captured and stored, for instance; politicians regulate markets in which digital corporations operate, and those same digital corporations lobby politicians directly to make decisions that they favour. Nevertheless, in my analysis at least and in the specific context of communicative politics, the dominant productive logics of both systems operate primarily on hypertext: one system interprets it as data, the other as discourse. 8. There are many ways that we attempt this, many tools and techniques that can help us download or scrape event-trace data, testing strategies, surveys and interviews—the field of digital research methods is growing. The methodological challenge is deploying these different tools in a way that recognises and responds to perspectival uncertainty.

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9. In online communication, ‘the interacting parties meet in time rather than in a place; for that reason, response presence becomes important, and temporal rules of coordination begin to matter’ (Knorr Cetina, 2009, p. 79). In order to fulfil the role designated to them, hashtags need to facilitate this meeting.

References Appadurai, A. (1991). Disjuncture and difference in the global cultural economy. Theory Culture Society, 7, 295–310. Bruns, A. (2019). Are filter bubbles real? Cambridge, UK: Polity. Bruns, A., & Burgess, J. (2011). The use of Twitter hashtags in the formation of ad hoc publics. Paper presented at the European consortium for political research conference, Reykjavik, Iceland. http://snurb.info/files/2011/The%20 Use%20of%20 twitter%20Hashtags%20in%20the%20Formation%20of%20 Ad%20Hoc%20Publics%20 (final).pdf Bruns, A., & Moe, H. (2014). Structural layers of communication on twitter. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and society (pp. 15–28). New York: Peter Lang. Bush, V. (1945, July). As we may think. The Atlantic, 176(1), 101–108. Retrieved from https://www.theatlantic.com/magazine/archive/1945/07/as-we-maythink/303881/ Carr, N. (2013). Digital dualism denialism. Retrieved from http://www.roughtype.com/?p=2090 Derrida, J. (1967). Of grammatology. Baltimore, MD: John Hopkins University Press. Halavais, A. (2014). The structure of twitter. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and society. New York: Peter Laing. Hassan, R. (2019). Uncontained: Digital disconnection and the experience of time. Melbourne, Australia: Grattan Street Press. Joyce, M. (2003). Siren shapes: Exploratory and constructive hypertexts. In N.  Wardrip-Fruin & N.  Montfort (Eds.), The new media reader (1st ed., pp. 613–624). Cambridge, MA: The MIT Press. Jurgenson, N. (2012). When atoms meet bits: Social media, the mobile web and augmented revolution. Future Internet, 4, 83–91. https://doi.org/10.3390/ fi4010083 Kafka, P. (2019). Mark Zuckerberg isn’t done answering questions about Facebook’s political ads policy. Vox. Retrieved from https://www.vox.com/ recode/2019/10/21/20925872/facebook-political-ads-russia-iranzuckerberg-press-conference

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Knorr Cetina, K. (2009). The synthetic situation: Interactionism for a global world. Symbolic Interaction, 32(1), 61–87. https://doi.org/10.1525/ si.2009.32.1.61 Lewis, J. (2005). Language wars: The role of media and culture in global terror and political violence. London: Pluto Press. Manovich, L. (2003). New media from Borges to HTML. In N. Wardrip-Fruin & N. Montfort (Eds.), The new media reader. Cambridge, MA: The MIT Press. Nelson, T. H. (1981). Literary machines. Sausalito, CA: Mindful Press. Pariser, E. (2011). The filter bubble: What the internet is hiding from you. New York: Penguin Press. Perlstein, R. (2012). The Long Con: Mail-order conservatism. The Baffler (21). Retrieved from https://thebaffler.com/salvos/the-long-con Pond, P. (2016). Software and the struggle to signify: Theories, tools and techniques for reading twitter-enabled communication during the 2011 UK riots (Doctor of philosophy). Melbourne, Australia: RMIT University. Ritzer, G. (2012). Sociological theory (8th ed.). New York: McGraw Hill. Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. New York: Basic Books. W3C. (2019). What is HyperText. W3C Glossary and Dictionary. Retrieved from https://www.w3.org/2003/glossary/ Wardrip-Fruin, N., & Montfort, N. (2003). The new media reader. Cambridge, MA: The MIT Press. Yasseri, T., Sumi, R., Rung, A., Kornai, A., & Kertesz, J. (2012). Dynamics of conflicts in Wikipedia. PLoS One, 7(6), e38869. https://doi.org/10.1371/ journal.pone.0038869 Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: Profile Books.

CHAPTER 9

Principles of an Interactionist Methodology

There is plenty of evidence that political discourse is becoming more polarised (e.g. Fuchs, 2018; Midlarsky, 2011) and in ways that are pervasively violent (e.g. Lewis, 2015). Furthermore, the lying that we now label hyperreal or ‘post truth’ is seemingly of a different type—it rejects objective validity and does not concern itself with reason or evidence. Commentators have likened it variously to Orwellian doublespeak and to Soviet propaganda for its apparently deliberate efforts to control or ‘destroy’ objective truth (Gessen, 2018; Kakutani, 2018). One assumes that politicians have always lied—at least since there was an electoral motivation for them to do so—but for a short while in a few countries, there was an expectation established that they would be asked to prove their lies and to justify their propaganda.1 In other words, there was some sense in which we acknowledged that lying violated some shared sense of what was real and true. It is this sense of shared reason—a basis of objective, empirically verifiable ‘facts—that seems so challenged by modern political lying. This breakdown in shared objectivity might be linked to digital media in many different ways. Much as we suspect that social media use may cause unhappiness, we suspect that digital media is messing with our politics in ways that we fear are detrimental, even if we don’t quite understand them. I have argued that the dysfunction is communicative—the normative deliberative ideals of western liberalism had been subverted by a vacuous, corporate and sometimes violent struggle to signify. This struggle, I suggest, involves a crude assertion of political power and has a © The Author(s) 2020 P. Pond, Complexity, Digital Media and Post Truth Politics, https://doi.org/10.1007/978-3-030-44537-9_9

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specific-material influence on the language we use politically. Some words and phrases become rote, some become unsayable and some mask a numbing chasm where meaning and morality would otherwise be. Habermas assumed that: ‘millions of fragmented chat rooms across the world tend instead to lead to the fragmentation of large but politically-focused mass audiences into a huge number of isolated issue publics’ (Habermas, 2006, p. 423). Modern political analysis frequently laments ‘echo chambers’ and ‘filter bubbles’, because social media channels traffic according to particular communication and consumption practices. I have already discussed the concerns that communicative speed and informational volume overload human capacity for deliberative consideration. I have also considered how certain logics may reinforce semiotic polarisation, and how ‘engagement’ metrics might reward particularly extreme signification. These arguments would tend to suggest that interaction between the digital and political systems is logically detrimental to the latter. Digital differentiation follows principles that are profoundly at odds with the higher ideals of deliberation, reason and equal representation that liberal democracies prize. As politics moves online, these logics reinforce quantified crudities, reward reductive acts of engagement, hollow out arguments and destabilise meaning. Nuance disappears and complexity is ignored as the news cycle winds itself in an ever-tightening coil. Political signification becomes more extreme. The daily experience of being political can be exhausting and dispiriting, the churn and chaos are dizzying. Survey the political landscape in 2019 and it’s easy to find evidence of this happening—vapid, dishonest election campaigns, political opponents seeming to inhabit entirely different realities and political power in the pudgy hands of celebrity charlatans. The machine grinds on everywhere, fuelled by the hidden flow of advertising dollars, churning out noise and nonsense, playing to the basest instincts of the political public. However, empirically speaking, most of these associations remain untested hypotheses because they do not lend themselves to easy validation. It may well be that fragmentation leads directly to polarisation but, because fragmentation online tends to happen topically or ideologically, it is incredibly hard to know if one is causing of the other—indeed, both may be unfortunate products of some other change in conditions. According to system interactionism, if we want to interrogate the relationship between digital media and political polarisation then we need to ask specific questions about the logics of system assemblage.

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In the final part of this book, I want to re-engage with the question of political polarisation, the type of political speech that polarisation clearly encourages, and ask what role digital media plays in shaping this type of discourse. Specifically, I want to frame, ask and then respond to a specific question about digital media, of a type that system interactionism can answer. This is an important distinction and we have surely established that general questions and sweeping answers are particularly ill-suited to a complex, interactive reality. I am going to assume that the sort of language I have described in British, American and Australian politics is a product of highly polarised discourse. While I am not claiming that causality is necessarily proven, there are good reasons to think that ‘post truth’ politics is strongly associated with modern changes in communicative exchange. System interactionism should facilitate our engagement with complexity, focusing our attention on the events through which reality emerges. It should make our analysis more granular and precise, and it should promote a particular type of knowledge, a synthesis of critical reasoning and empirical evidence, more confident but also more careful. System interactionism is meant to be the study of change: it describes how change is happening and analyses the relative systemic influences shaping that change. Describing logical precedents is only step one in the interactionist methodology. We have established precedents to locate and limit our study, we have some sense of how the relevant systems might process events, but we haven’t even begun the work of observing interaction events.

A Question of Empirical Principles Interactionists prioritise systemic change, observe events, describe recurrent patterns within those events, analyse those patterns logically and then compare logical patterns between systems. Our questions have to predicate that type of enquiry. So what question do we want to ask? What is the right way to frame our wide-ranging concerns over the influence of digital media on political communication? The question must reduce complexity, direct and focus our attention within the interaction field, while representing those concerns logically. The simplest way to approach this task is to locate our question in respect to the interacting systems that it concerns: we should specify a digital, political and hypertextual context for study. Let’s frame the question as concerned citizens, worried that polarisation in political discourse is having a pervasive and damaging effect on the

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liberal democratic order and equally concerned that the digital media environment is exacerbating this polarisation. I think that we have reviewed sufficient evidence to establish that these are valid concerns, even if the causality is not yet certain. Political language is increasingly shaped online, discourse is highly polarised—both in terms of extreme signification and the fragmentation of political publics—and there are logical reasons to argue that the systems are incompatible. Of course, it is insufficient to say that discourse is polarised without specifying exactly what polarisation means and how it operates linguistically. Polarisation can be characterised across a traditional left-right political axis, but it can also mean division within several thematic and ideological frames. High polarisation is typified by extreme language, often relating to familiar archetypes or signifiers (Lewis, Pond, Cameron, & Lewis, 2019). The following question should suffice to frame the discussion that we still need to have: Do some ethno-nationalist discourses become polarised more rapidly than others on Twitter and, if so, why? The benefit of this question is that it allows us to develop a comparative study (of different discourses) within the same system-set, which means that that we greatly reduce the potential confounding complexity that might otherwise exist. One of the major challenges of interpreting polarised discourses is that there exists no external semiotic reference scale for evaluation. The language of online extremism is sometimes obtuse (full of in-jokes, memes, self-references and coinages), highly context specific and prone to change. If we attempt to evaluate extremism ‘outside of itself’ then we are quickly confounded by semiotic deferral. However, if we focus our analysis on temporal changes within topical and local discourse communities—exactly as system interactionism proposes—we can identify directional trends and compare these trends by degree and by speed. We can then ask comparative questions of thematically aligned discourse (e.g. øt = øt+/−1) and track changes in discourse over time. In effect, the speed of change in polarisation becomes the key indicator of shifting assemblage patterns. Our framing, then, is largely concerned with the identification and description of the topical discourse community that we want to study— the agents of political signification and the context in which they are operating. One area in which there has been alarming activity, for the best part

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of a decade at least, is in the reframing of the western national imaginary away from liberal multiculturalism and towards conservative ethno-­ nationalism (Bieber, 2018; Luce, 2019). While many nation states—not just those with white majorities—have always harboured proponents of ethnic or ancestral citizenship, recent years have witnessed a resurgence and a mainstreaming of these ideas. Trump, Bolsanaro, Johnson, Orban, Putin, Modhi, Duterte and Morrison all represent, in their own way, a version of exclusionary nationalism predicated on ethnic or religious identity. These ideologies are particularly concerning because many of us assumed that we had rejected them for good following the catastrophic nationalistic violence of the twentieth century. So much for history. As a result, researchers and policy makers have invested considerable time and attention investigating these particular discourses, many of which are perpetuated through online media (Conway, 2017). Personally, I spent a year in 2017 working on a project for the state government, analysing how social media discourses were challenging social cohesion and multiculturalism in Victoria, Australia. Many of those discourses, we found, were perpetuated through a narrow range of archetypes that ‘revolved around these cultural and political tropes of heroic victimhood and the violence of exclusion’ (Lewis et al., 2019, p. 973). We also found that the discourse community, which we were examining in a local context, restricting our analysis to exchange around Australian television programmes, was distributed globally and frequently borrowed language from the MAGA movement and the Brexit campaign.2 What we didn’t address in our work was whether or not the communicative conditions of social media might be contributing to polarisation within the discourse. How could we have done so? If we were pursuing interactionist empirics, and having identified a topical context, next we would have looked to specify a constrained digital system to further locate our study. Polarising nationalist discourses are everywhere online, including on all the mainstream social media sites, on Reddit and YouTube, on the forums and chatrooms you have likely heard of (4Chan, Gab) and on those you may not have heard of yet (8Chan, Bitchute). In our study, though, because we were interested in discourse circulating in the mainstream during broadcast television shows, we focused our attention on Twitter, which despite its relative lack of growth compared to other social media sites, remains an important domain for explicitly political discussion and activism. Let’s continue that focus for now because, in addition to being a political place, Twitter also remains a

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relatively ‘open’ environment for researchers. Unlike Facebook, Instagram and YouTube, it is still possible to access Twitter’s application programming interface (API) and to retrieve data on the interactions that happen there.

Questions of Perspective Mention of the API is timely because, once we have framed our study logically and topically, the next step is to consider how perspective might influence the empirical observations we are about to make. In digital media research, the API is usually central to that conversation. An API is a public portal into the data that a web application collects on its users and its own logics of assemblage. Web companies build APIs to ‘open’ their interfaces to third party developers to build client-applications that extend the functionality and services of the primary application. Technologically speaking, that can sound a little more daunting than it actually is. An API is just a web address that points to an entry point to the website’s servers and protocol for writing queries to that endpoint. A developer writes a query to the API using the appropriate syntax and the API responds by returning the requested data in a standardised format. In many respects, an API functions just like an advanced web-search tool: the user specifies a series of parameters that limit the information that the search returns. Often, the API is the perspective from which digital media research is conducted. There are some obvious reasons for this. First, an API is a readymade access point that returns data that the researcher can use immediately. It is far simpler to ask the application how it works than it is to invent an ‘external’ method for tracking interactions. Second, an API can give the impression of being an ‘all-seeing’ view of the system.3 Often in digital media research, we begin with an API search that we assume is capturing all the potentially relevant data, and then we search within that data for the events that we judge to be relevant. Third, and a little more confusingly, an API is normally a black box (Driscoll & Walker, 2014), meaning that we are never quite sure how the system is deciding what data it returns to us, whether or not this is all the data, or even if it is a representative sample. This might seem a little paradoxical, but the fact that we cannot know what is happening within an API means that we cannot question it—we have to proceed on the assumption that it is representing the system fairly and construct all our knowledge on the basis of that

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assumption. Fourth, there are not that many alternatives to an API for collecting data on a digital system at scale. Browser-based web scraping can be automated to maximise data collection, but it is far less efficient than sending a query to a web address and then waiting while the data downloads. APIs offer the most efficient, most available access to digital interaction data that we have, and so we tend to use them whenever we can. There are some compelling reasons, then, for concluding that an API is an appropriate perspective from which to begin observation of a digital system—we might even conclude that it is a perspective that we should prioritise above all others—but we shouldn’t reach either conclusion without first asking, how does it work as a perspective on the assembling system? This is a particular type of question that system interactionism demands. It goes beyond wondering how representative an API data sample might be and insists that we view the API as an integral and limited component of system assemblage. There are several ways in which we should question the limitations of an API. We should also be asking what alternative perspectives are available on the system generally (and on the specific contextual meanings that we are investigating). If we work through these issues in turn, then perhaps we can illustrate a couple of things. First: how can system interactionism reveal the potential and the potential limitations of the API for digital research? Second: how can an API perspective be better integrated into an interactionist account of system assemblage?

An API Is a Multiplicity of Different Perspectives, Not All of Which Will Be Realised No API delivers on the promise of total system access, and most fail to deliver on this promise in multiple ways. First, most publicly accessible APIs are restricted, so that they only return a sample of the total amount of data collected. For instance, Twitter’s streaming API will not return more than 1% of the total number of tweets published in any period, and researchers must work on the assumption that Twitter is selecting this sample randomly. Researches have investigated how representative APIs are of the systems that they are meant to describe (e.g. Morstatter, Pfeffer, Liu, & Carley, 2013), but I judge representation to be a relative minor issue. For many topical queries, the 1% limit is never reached because the topicality itself is more limiting—people publish a lot of tweets on a lot of

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subjects, generally speaking only a few of them speak to our specialist political interests. A bigger issue is likely to be that the system itself does not record, store or make available information on all its interaction events. This is particularly an issue with propriety services and advertising data. For instance, even when Facebook maintained a public API, the company only ever offered access to a limited array of data sources. Information on some of the most influential logics within the system was kept private, sold to paying clients or perhaps never recorded in the first place. It’s important to remember that a system collects data in response to coded commands to collect that data. If the engineers have not specified that data should be collected, then often there is no record of the event to consider. This raises a curious ontological question of course, but not one that we need to consider here. Finally, perhaps the biggest and most easily grasped issue with the API perspective is that it is always constructed in response to the query that the researcher submits. The simplest way to think about variable perspectives within an API is to remember that the data an API returns is a response to a specific query sent by an external application. Different queries to the API encode a different way of ‘seeing’ the system. A call that requests tweets containing keywords, for instance, directs attention very differently from one that requests tweets from a list of connected user accounts. These different types of call construct very different empirical perspectives on events happening within the system. (Pond, 2020)

Too often, I think, when we are working with systems that collect data on themselves, we overlook our own role in shaping the system-as-data that we eventually observe. This is probably because critical and social theorists rarely work directly with data structures and database queries themselves—there is a separation of expertise that means social researchers leave such tasks to programmers and this complicates matters. Undoubtedly, though, if we are using an API to collect data on a system, then the structure of our search query has enormous influence on the direction of our study. If we return to our question about polarisation in ethno-nationalist discourses, it should be fairly easy to illustrate how a query to the API can logically influence the representation of the system that the API returns. If

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we want to retrieve examples of this discourse from the Twitter API, then we have some different options, depending on what we know already about this specific set of meaning-making processes. For instance, we could search for tweets that contribute directly to this type of discourse, using keyword searches, hashtags and specific signifiers to identify them. Lewis et al. (2019, p. 966) report that these discourses are characterised by a list of highly politicised signifiers, most of which relate to crude ethnic, religious or ideological binaries. To this keyword list, we could add various thematic categories, which as Jones, Trott, and Wright (2019) describe, frequently circulate in male-centred and nationalist forums on the internet. We could also consult the latest reporting from journalistic organisations (e.g. Right Wing Watch) and civil society watchdogs (e.g. American Civil Liberties Union) for trends in extreme discourses, both on the right and on the left, that might be pertinent. In short, we could build a search strategy aimed at collating topical discourse, which we would then have to interpret in terms of meaning production. This type of strategy is common in digital media research, which frequently frames a system through its association with a single keyword, hashtag or a short list of search terms. Sometimes, this makes relatively good sense. Bruns and Moe (2014) have described how hashtags are used to coordinate publics on Twitter around a shared or topical interest. I discussed the framing potential of the hashtag in the previous chapter. However, even when there is a clear and generally accepted convention to use a hashtag to coordinate discourse (academics love to do this during conferences, for instance), this still represents a dramatic reduction in system complexity. Essentially, we say that we are only interested in how Twitter operates in a highly abstracted sense for a (usually) small group of people engaged in a type of meaning-making that is atypical for the system. During a conference, even the most focussed academic is unlikely to engage only with the specified hashtag. This would require muting all other tweets, essentially ‘switching off’ the system beyond this immediate operation, rejecting the affordances that typically define the interactive experience. So, while topicality is a common search strategy, it is not necessarily ideal for framing a digital system, and there are alternatives. Meaning can be approached differently—as something produced through the collective actions of a group of interactive political actors. Such an approach, which preferences users and their relationships with each other, interprets meaning as being a connective structure, and predicates a very different search

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strategy. Instead of sending keyword requests to the API, a researcher will have to establish a list of candidate accounts and make those accounts the foundation of the query. Ideally, the list would include every member of the ethno-nationalism public (however we define it) but this is not strictly necessary. Studies of online conversations often begin with a list of accounts that they imagine to be particularly influential or representative—so called opinion leaders or shapers—relying on a two-step communication model to explain how society collectively makes meaning (Katz & Lazarsfeld, 1955). Presumably, if you can work out what these opinion-shaping accounts are saying, then you have a shortcut to the wider, distributed meanings of society at large. The limitations of such an approach should hopefully be fairly obvious, but the main concern for me is that it assumes an unchanging systemic configuration—a group of people who ‘lead’ meaning-making regardless of temporal or topical change. Of course, a list can be updated contextually but not if we are using the list as the basis of an API query to define the context. In the Covington example that I discussed at the start of the book, the ‘influential’ account spreading the video had an unknown social status, was probably a bot and would be unlikely to make a candidate list based on follower numbers alone. Specifying context through any sort of a priori account list seems to ignore important dynamics in how the digital system assembles. Is a more heuristic approach possible? Perhaps different network relationships can be used to extend the public (e.g. follower-following status or interaction history) in ways that make sense given the broader topical interest. Or, given an initial topical data set, an analysis of account activity combined with those network relationships could extend the public in ways that are both systemically and contextually sensible. Where this leads, presumably, is towards an iterative style of searching for data, in which an initial query targeting topical meaning-making (or a user-defined public) is extended by subsequent queries that respond to events identified in the data. In our study of polarisation and extreme discourses, we began to formulate an approach like this, first building a corpus of ‘extreme signifiers’ from an analysis of established political discussion spaces. We then used these signifiers to identify an initial Twitter data set, sending a series of queries to the API targeting these signifiers and a range of topical ‘anchors’ or hashtags—that is, topical subjects around which discourse was polarising. The word ‘Covington’ would work like such an anchor for the

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example with which I opened the book, but so do the names of particular politicians, events like elections and processes like impeachment. We used this initial data set to identify accounts that were particularly active in the discourse or accounts that had a disproportionally large number of followers. We considered these accounts to be independent ‘active perspectives’ on the meaning-making process, and then we sent another set of queries to the API seeking various account-system interaction events. In other words, we went looking for individual account contributions to discourse, but also account-specific interactions with that discourse—that is, any evidence of the account interacting with the discourse in a perspectival fashion. These various rounds of query-review produced a dataset of tweets in which every record was assigned an interaction type and an attribute locating it in terms of perspective. Working to the theory that reality is best approximated through the sum of perspectives, we began modelling formulae for synthesising tweet-centred meaning-making according to the weight, centrality and persistence of the perspectival account data. It’s an approach that offers certain advantages. For instance, it becomes possible to ‘stratify’ the systemic production of meaning using clearly aligned account-sets and to probe the influence of different perspectives on a shared impression of system production. Both developments permit a far more nuanced discussion of how the system is differentiating and collectivising the production of meaning (e.g. see Pond & Lewis, 2019).

Making Sense from Multiple Perspectives We are still only dealing with a limited number of perspectives framed in a narrow way through the Twitter API, and we are still reliant on the system itself to record how it is interacting internally and externally. However, we have advanced from where we were initially, imagining that the API could provide a single, unifying ‘meta-perspective’ on system assemblage. We are also able now to consider how different perspectives can reveal different assembling logics. By combining topical and network queries, we can highlight interaction sets involving different users and trace the influence of those interactions, even if we cannot yet fully access the experience of those different perspectives. For instance, the type of data stratification that I have just proposed enables a researcher to highlight and extract events that directly involve a user account and a specific set of signifiers. The researcher can then describe how meaning-making through the

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digital system appeared for that user and—if the necessary models have been built and the influenced assessed—consider how those user-specific events ‘echoed’ across the system. Moreover, if we have calculated and calibrated those models correctly, we will be able to ‘sum’ the meaning-­ making experience across perspectives and deliver an empirical account of assemblage for the system as a whole. So what does the ‘calculation’ and ‘calibration’ of a systemic assemblage model involve? We want to combine different perspectives into a multiplicity that describes the system as a whole, but what should be the formula for doing so and, more fundamentally, is it even appropriate epistemological or ontologically to combine perspectives in this way? I should say, first, that my work in this area is still limited and experimental. It is relatively easy to pull at the perspectival threads of a system, but once we start, we can find the unravelling difficult to stop. My argument has always been that in our attempts to deconstruct and then reconstruct a system, we can learn something fundamental about its operation.4 I should also say that the work of system modelling is dependent on the conceptual tools that one can bring to bear on the task. The empirical methodology I have described delivers a multi-perspectival account of a system—it does not lead a researcher towards a particular method for analysing or interpreting that empirical data. However, I think it is worth describing quickly what assemblage modelling looks like for me, given the skills and the tools I have at my disposal. I am a cultural theorist but also a computer scientist and a statistician—I seek ways to recombine perspective data using weighting and probability. I like to marshal quantitative data in support of critical reading. As such, I am drawn to the idea that the calculation of a ‘system model’ begins with an initial calculation (or description) of systemic meaning production, which is then adjusted (or calibrated) in response to new data from alternate perspectives. Consequently, the sort of modelling that I undertake looks very Bayesian, in that I begin with what I believe to be a reasonable approximation of systemic operation and then make weighted adjustments to that initial description based on reliability and the influence of new perspective data. For instance, imagine that we deem it likely that ethno-nationalism is more extreme in certain digital systems and we want some way to compare discourses across systems. This means that we need a way to describe or to measure extremism in discourse within those different systems,

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which in turn means that we need an assemblage model for each system— a way of understanding ‘meaning’ that accounts for both: • The semiotic (hyper)textuality of political signifiers (‘universal’ meaning). • The allocation of attention between signifiers within the system (perspectival meaning). In our study, we are likely interested in comparing Twitter to other social media systems, say Facebook and Reddit, but I am going to describe the process for Twitter. We begin with the assumption that all text produced through the system is equally meaningful. In other words, our baseline measure of extremism, our prior, is calculated using all the texts from all the tweets that we deem topically relevant. It doesn’t matter exactly how we ‘calculate’ how extreme the ethno-nationalist ideas are on Twitter, but the process will involve the identification of key signifiers and then their categorisation and arrangement against polarisation scales.5 Once we have described the full sum and range of meanings produced by the system, we can start thinking about how the system allocates attention between these meanings, and how the interplay of different perspectives might influence that happening. The first step in the process is obviously to work out what the different perspectives might be and to collate the relevant interaction data for each of them. In the Twitter context, that will mean a full record of the tweets, retweets, replies, likes and comments for each account, but will not extend to direct messages or other click-through data that is unavailable through the API.  Once we have a list of the available perspectives, we can begin to differentiate between them based on the multiple metadata fields embedded in each ‘event-object’. We are looking for variability (leading to differential influence) in the construction of perspectives. Therefore, we may reason that the total number of followers an account has is an important variable. Alternatively, how active the account is within the discourse—that is, the total number of interactions it initiates—may be more significant. We list these variables for each perspective and then begin the process of variably weighting their contributions. This is the Bayesian part of the process. The approximation and adjustment of weights is recursive and gradual—we try to pick apart the influence of different perspectives based on how the system responds to their action. That might sound a little confusing, but the idea is relatively

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simple. When an event happens, the system can only respond through further events—that, after all, is a fundamental principle of event-based assemblage. An interacting sub-system (a perspective) can exert influence on systemic assemblage as long as its interaction with the system precipitates further events. The more event-responses that it can initiate, the greater its potential to influence the production of meaning within the system. Influence is therefore realised and measured through causal chains of interaction-events. Ultimately, this means that there are two primary mechanisms through which a sub-system can increase its influence within the wider system. It can increase its own activity, creating more initial interaction events (by publishing more tweets, responding more frequently and more energetically) and thus increasing the opportunities for the system to respond. Alternatively, it can better align itself with the differentiation logics of the wider system. As discussed, alignment with logical Twitter tends to mean becoming ‘more popular’, at least in terms of the programmable metrics that the system uses. That might mean that an account increases the number of followers it has, gains ‘verification’ or otherwise convinces the algorithms to promote its tweets beyond its immediate ‘follower’ audience. In other words, events involving ‘popular’ sub-systems—as defined by a series of Twitter-centric metrics—produce exponentially more subsequent events. This is the sense in which influence within the Twitter system is constructed through causal event chains. Influence, or the ability to shape the logical assemblage of the system, is the product on an initial event (or event set) and the series of ordered events that ‘flow’ on from that initial interaction. Influence is therefore computable, as long as subsequent events can be ordered and arranged relative to the initial interaction. In the simplest sense, influence is a ratio of initiation to response: an event that produces 10 reactions is equally as influential as an event set (n = 10) that produces 100 reactions.

The Temporal Denominator Of course, the systemic response is always more complex than this, and influence can rarely be reduced to linear ratios or single-step chains. Even relatively simple systems interpret events in multilinear processes (which aggregate in complex multi-dimensional structures) and through divergent logics that often operate competitively. Anyone familiar with a social media system like Twitter will be aware that the system is sub-divided and

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stratified in complex and confusing ways: there are cliques and communities just as there are overarching discourses that pit one group against another. The work of tracking influence across perspectives and through myriad response pathways is clearly complicated and—as I have been arguing throughout this book—impossible without a fully temporal understanding of assemblage. In a very practical sense, we discover that interactions only become meaningful (both ontologically and epistemologically) when they can be placed in relation to other events in logical chains. Time is absolutely fundamental to that ordering and to its analysis. What does the temporal analysis of causal event chains happening across multiple perspectives look like? This is an open question, of course, and not one that I am going to answer in this book. The principle that I stated at the end of Chap. 5 still holds, as long as temporality is conceived and studied through a single perspective. We aim to observe event-driven change within system configurations. Fundamentally, temporal analysis concerns itself with change within systemic histories and projects its analysis into systemic futures—it records the becoming of the assemblage. This means that the system observed at time t is interpreted in relation to earlier (and later) configurations of itself—analysis is rooted by the persistence of the same system-observer relations through time. However, once multiple perspectives and multiple ‘dimensions’ of assemblage are considered, then clearly it becomes significantly more difficult to compare t to t +/−1 because multi-linearity challenges alignment within the system. Once casual chains branch logically and fragment ‘perspectivally’ then the simple ordering of t, t1, t2 and tn no longer holds, because each event is ordered temporally and perspectivally at the same time, and there is no straightforward way to synchronise temporalities across perspectives. This is confusing so it may help to illustrate the complexity with an artificially simple example. Imagine an assembling process that involves three events in a causal chain: E1, E2 and E3. The initial event (E1) always provokes a single response from the system, a second event (E2), which in turn is followed by a third and final event (E3). I say ‘always’ to emphasise that the system responds the same regardless of the initiating sub-system or the observing perspective: the logics of the system are rigid, 1 always produces 2, which always produces 3. In Chap. 5, I raised the problem of probabilistic observation (which quantum scientists call super-­positioning) and then immediately side-stepped it by accepting the convenient assumption that system configurations are normally distributed unless proved otherwise. By asserting that the system always responds to E1 with E2 and

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then E3, we confirm that the system assembles independently of observation, which is phenomenologically unlikely but far more convenient for this introductory discussion. In effect, we have removed two dimensions of complexity from the computational challenge of temporalised analysis. Each perspective observes the same ‘single-state’ system and the system responds to each perspective in exactly the same way.6 In other words, through our convenient assumptions, we have hypothetically controlled the internal differentiation of the observed and observing systems. As a result, we can approach the simple event chain, E1, E2 and E3, from two different perspectives and assume that E1a = E1b. However, this does not means that we can assume that E1a, E2a, E3a = E1b, E2b, E3b. While we may be confident that the order of the events is the same in the two chains, we cannot be sure that the temporality of that ordering is equivalent. I italicised temporality in that previous paragraph to try and emphasise the centrality of time to the entire project of system interactionism. This centrality demands that we engage with fundamental questions about time, our theoretical understanding of the phenomenon and our experience of it. Considerable complexities arise from a fully temporal consideration of assemblage across different perspectives, but I hope to show both that (1) recognising these complexities is absolutely necessary empirically but also that (2) addressing them can reveal far more about the dynamics of system assemblage than a non-temporal analysis ever can. The fundamental problem of time, empirically speaking, is that temporality is created within interactions between systems. That means that two perspectives interacting with a central system each create their own times, because even if the main system always differentiates in the same way, by definition the different perspectives do not. Any difference in the ‘pace’ of differentiation between perspectives will influence the production of time relative to the main system. As such, it only makes sense to speak of the temporality of the Twitter system as an assemblage of the multiple temporalities flowing through the multiple perspectives through which the system is realised. If we want to analyse assemblage across multiple perspectives, then, we must somehow synchronise the temporalities of these different perspectives, because otherwise we will never manage to order event chains relative to each other. In other words, temporality must be a denominator in any computation of system synthesis—the more complex the system, the greater the challenge of synchronising embedded

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temporalities. Even minor variations can massively disrupt synchronisation across a set of system-perspectives. The challenge is considerable. Even minor variations can massively disrupt synchronisation across a set of system-perspectives. Clearly the first step is to ensure that we have described fully the temporality of each perspective. We cannot compare or synthesise Ea123 and Eb123 unless we have first fully documented Ea123 and Eb123 individually. As this point, we must confront an existential question in time studies: how can two times, constructed ‘subjectively’, be compared if there is no overarching or universal time to situate them? It was this question, ultimately, that prompted Newton to theorise t—the universal temporal container for all the relative temporal happenings in the universe. Obviously, there are issues with continuing to assume that t is a predictable and universal force but perhaps we can still use the clock—our most familiar t-inspired technology—to pace the relative assemblage of different perspectives. As long as we can assume that a clocking system is operating independently of the system set under study, and as long as we can rely on our clock to repeat a regular pattern of differentiation periods, then we can compare our system-perspectives against the clock, and use this relationship to describe the embedded temporalities of different perspectives. Now, admittedly, there are complications here that I am not addressing, and some assumptions that may not hold, but let us accept the possibility of independent pacing at least until we have illustrated the essential mechanics of temporal comparison and calibration. Once the essential principles have been established, we can worry about the problem of universal and local times (but not in this book, thankfully). This means that the role of an empirical observer essentially involves synchronising events from different perspectives using the independent temporal reference scale. Before this can happen, however, the observer must document the order and spacing of events within each perspectival chain. The diagram below illustrates how variability can arise between perspectives, even if the ordering of the E123 chain is ‘fixed’ across perspectives. As I said earlier, we cannot be sure that the temporality of ordering is the same and the primary reason for this is that different perspectives may produce different times even from a standardised set of system interactions. In the simplest terms, the reason for this is that different perspectives may produce time internally at different speeds. This can be hard to picture, but I think we can understand the idea intuitively. Consider these

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two different experiences of waiting for a clock to tick through 60 seconds and mark a minute. First, imagine that you are watching the clock intently—all your attention is focused on the second hand working its way slowly around the dial. We could say that minute is 60 events in production, but we know also the painful waiting for a watched clock, the heaviness of the second hand, the apparent slowing of the experience to an agonising crawl. How much longer does the minute become if our internal system is under stress, holding our breath for instance or an uncomfortable yoga pose? Alternatively, think how a fleeting a minute can be if our attention is elsewhere, roaming over a crowded table of friends or absorbed in a spectacular action movie. The stimulus makes the time fly, the minute streams by in a flood of events: flashes of light, emotion, recognition and reaction. Undoubtedly, in the second scenario, the minute has ‘contained’ many more events than 60, and in effect we have produced more time, but our engagement with it as time has barely registered. Time can work differently even if the set of events being experience is the same. Interactions within our own consciousness, between our own liminal and subliminal experiences, are sufficient on their own to create entirely different temporalities of being. Of course, we cannot access the subconscious experience of different perspectives during our empirical observation, be we can note how different interactions within each perspective might be ordering local temporalities for these different sub-­ systems. We can then use the ratio of relative differentiation rates (roughly, the number of intra-system events for an inter-system event) to further adjust the weighted influence of the different perspectives. GH Mead’s solution for how perspectival experience could be reconciled with universal reality was simply to add them up. Time t across the universe is the sum of all the times tø experienced locally within the universe. In terms of systems, we say that the universal system is the sum of every interacting sub-system within it, and that universal system time is the sum all those relative differentiation rates. Only meaningful interactions are relevant, however. Remember that events have to contribute to the production of meaning to be intra-system relevant—an event that does not register as meaningful does not register for the production of time either. So, the study of time production within a system (or sub-system) is essentially the study of the rate of production of meaningfulness within that system. This means that we frame, observe and interpret events, both within systems and between systems, in reference to the cumulative

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production of systemic meanings. This idea may be easier to grasp with a simple example. We are working with a question about ethno-nationalist discourses and rates of polarisation on the micro-blogging service Twitter. For the purpose of this illustrative example, let’s assume a couple of things. First, there are only two variants of ethno-nationalist discourse: one pertaining to race that involves a struggle to append the adjective ‘white’ to all forms of nationalist expression, and the other pertaining to religion, which seeks to elevate the ‘Christian’ signifier above all others. Second, let’s assume that the global ethno-nationalist community consists of just two people, Pauline who lives in Australia and Boris, who is British. We want to know if Pauline and Boris are becoming more extreme in their use of the ‘white’ and ‘Christian’ signifiers as they use Twitter. Fortunately, because the public is so small and its discourse is so limited, it is relatively easy to identify different perspectives within the system. We have Pauline and Boris to consider, and we have two hypertextual-­ system-­perspectives defined by the key signifiers (there are others too, but these initial four will be plenty for this discussion). We send a series of queries to the Twitter API: first we ask for all of Pauline’s tweets containing the word ‘white’ and then for all her tweets containing the word ‘Christian’. We repeat these queries for Boris so that we have four sets of tweets (Pw, Pc, Bw, Bc) that we can order individually using the Coordinated Universal Time (UTC) timestamp value that Twitter includes in the metadata of all tweets. Now, to make things even simpler still, let’s assume that there are only three ‘degrees’ of polarisation in ethno-nationalist extremism: mild, moderate and extreme. We judge each tweet individually using some sort of semiotic index of extremism and assign a mild, moderate or extreme value. Somewhat fortuitously, it turns out that both Pauline and Boris have only sent three tweets within each of the discourse categories (imagine that!), and so our perspectival chains look quite familiar. Pauline’s contribution to white nationalism discourse, for instance, involves three tweets exhibiting increasing extremism: Pw1-mild, Pw2-moderate, Pw3-extreme. We have identified and sequenced the events through which Pauline and Boris have interacted with these specific discourses on Twitter, but we have not yet fully temporalised their different perspectives. Pauline and Boris are different people and their interaction with the Twitter system may be different. How can we account for this? One thing we can try to do is contextualise these discourse sequences within Pauline and Boris’s

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overall experience of using Twitter. We return to the API and download all the tweets that both Pauline and Boris sent across the entire study period, regardless of topical context. Using the same timestamp value, we can sort these additional tweets into a timeline, so that we have a clear idea of how often Pauline and Boris are interacting with Twitter in between the discourse-specific tweets. The result is easier to illustrate than it is to describe. The diagram below illustrates the temporal sequencing of tweets, including discourse-relevant tweets for both Pauline and Boris. Dots represent the individual tweets and are plotted against an independent time scale, t (Fig. 9.1). The sequencing of events in this way, and their location within a fuller temporal description of system interaction, is important because it reveals how each perspective is allocating attention to discourse-specific interaction events. As we have established, meaning-making depends upon the allocation of attention between signifiers. As such, meaningfulness for each perspective—that is, for Pauline and Boris individually—is a product of discourse-specific signification and the attention (ratio) that each allocates to these specific signification events. In other words, what is meaningful for Pauline during the period t0 to tn depends upon where Pauline’s attention is focused in terms of signification and expression in the hypertextual cloud. We can only observe her focus in the act of signification: while Pauline may be changing her mind regularly about her ethno-nationalism, that variation only becomes meaningful for the Twitter system when she interacts with hypertext. In this

Fig. 9.1  The temporal sequencing of tweets, including discourse-relevant tweets for both Pauline and Boris

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example that happens when she encodes those views into tweets and publishes them. How often that happens, as a subset of her total interactions with the platform, tells us something about how much of her Twitter-­ specific attention is focused on signification within ethno-nationalist discourses. If discourse-specific tweets represent all her Twitter activity, then we can assume that her Twitter-specific attention is wholly focused on her (increasingly extreme) expressions of white nationalism. If there are multiple tweets in between these discourse-specific events, then we may conclude that her attention is wandering more widely, which would suggest the whiteness discourse is somewhat diluted, and may be a little less meaningful to her. In addition to this information about where her attention is focussed, we can also assess semantically what the focus is for Pauline. In this example, we have reduced the semiotic reading to a three-point evaluation of extremism but in practice we would engage in a far more nuanced interpretative reading. Pauline’s sequential signification chain shows us how (and in what order) Pauline’s attention is being redirected between signs. The speed at which this redirection happens, relative to Pauline’s overall Twitter activity, is important context for reading the internal differentiation processes through which she is making meaning from her interactions with Twitter. Our challenge, though, is to go further and, in addition to considering Twitter interactive influence on Pauline, to ask how Pauline’s experience and Boris’ experience and the systemic response to their actions shapes the production of meaning in the system overall.

Temporal Sequencing and Assemblage As discussed, the recombination of perspectives is largely a process of synchronisation and weighting: we want to know how Pauline and Boris are making meaning, when they are doing so, and how their different actions are likely to affect discourse at the macro system level. This means a couple of things. We have to consider other perspectives within the system that are not Pauline and Boris, and we have to find a way to synchronise Pauline and Boris’ interactions both with each other and with the wider system. That there are other perspectives even in this very simple example, in which we have already accounted for both contributors, may be a little surprising, but perspectives in the interaction field are features of systems not humans and may be material. In other words, we have to think about

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how the system ‘sees’ and responds to Pauline and Boris, both of whom are really just perspectival conduits in the struggle to signify politically. Ultimately, we are seeking a view on how the system is interpreting their tweets logically as discourse, which requires that we first have a sense of the relative weight of their contributions. Our aim is not to parse the systemic response to Pauline and Boris as individuals but as a discourse community, so we must gauge how their tweets are being combined as discourse. Synchronisation is only possible because of event sequencing and our assumption that an independent temporal reference is possible. The scale t0 to tn acts like stable ground, allowing us to stand still and observe the systemic world in relative motion around us. It allows us to align Pauline and Boris’ tweets with each other and with the technology, which marks its own differentiation against UTC also. In other words, we need the t0 scale to pace the publication of tweets within perspectives, to synchronise tweets across perspectives and to evaluate the systemic response to those meaning-making events. Time is our only tool for orientating the interactive triad and for parsing relative contributions to the meaning-making process. Imagine that Pauline and Boris send their tweets in intervals as shown in the diagram below (Fig. 9.2). If the two perspectives were identical— that is, if Twitter discerned no difference between Pauline and Boris—it would be easy to describe the respective discourses during each of the intervals because it would simply be the sum of the individual contributions. The bottom row in the table illustrates this outcome. For all but the final interval, discourse on Twitter is wholly reflective of signification in

Fig. 9.2  The combined impact of Boris’ and Pauline’s tweets on polarisation within the discourse

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the individual perspectives, either because there is only one contributor, or because the contributors agree. Only in t5 is there any complexity because both Pauline and Boris are contributing to the same discourse stream, only they are signifying different things. In this scenario, the resulting discourse would be between moderate and extreme, because neither Pauline nor Boris exerts more influence than the other. In reality, though, two perspectives are never identical and even this very simple example can cope with a little complexity that will arise from drawing some distinctions in between the accounts. Imagine, for instance, that Boris has ten times more followers than Pauline does. In this scenario, it wouldn’t make sense to assume that signification was equal across perspectives. Boris’ tweets will initiate a systemic reaction tenfold larger than Pauline’s, appear in ten times the timelines, most likely initiate more replies and retweets and presumably be far more influential in terms of systemic discourse. This crude measure of popularity is received by a logical system that is geared to reward and reinforce it. Boris’ larger number of followers means that, no matter what he says in his tweet, it is far more likely to be influential than anything Pauline says. As such, in t5 when both accounts are tweeting about Christianity and ethno-nationalism, the system discourse is far more likely to tend extreme following Boris than stay moderate like Pauline. There will be multiple points of difference between two perspectives. While Boris may have more followers, Pauline may be more central within influential networks, have particularly influential followers of her own or be able to ‘borrow’ popularity from another system in which she has more influence (although, admittedly, each of these scenarios would normally translate into more Twitter followers). If we are to synchronise and combine perspectives across the system, then we will need to weigh each of these differences, adjusting our Bayesian models to account for each one and for the interaction between them. What does it mean to ‘weigh’ the differences between perspectives in terms of a Bayesian model? Essentially, it means that we note the many differences between Pauline and Boris and then ask how influential each difference is likely to be given what we know about the differentiation logics of the Twitter system. For example, in their analysis of how Twitter shapes political discourse, Pond and Lewis (2019) explore factors that propel tweets towards ambience, which is their concept for identifying ‘macro’ system-meanings and the processes that produce them. They identify two factors—the number of contributors to a hashtag and the

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number of followers that each account has—as being particularly influential over the production of discourse and they compute a metric that they call persistence: A higher persistence score suggests two things: first that there is a high degree of plurality within the hashtag (several unique contributions) and second that there is a large primary audience for the tweets within the stream. Furthermore, because the distribution of followers to users tends to adhere to a power curve, a user with a large number of followers (a ‘prestige’ user) has a disproportionate influence on the persistence stream. This reflects how Twitter’s software-structural affordances translate into communication-­ system dynamics. (p. 224)

In effect, persistence adjusts system discourse away from the assumption that all perspectives are equal and towards a differentiated model, in which the popularity of individual user accounts has enormous influence over what discourses become ambient. Persistence assumes that these differences are translated directly into ambience but, of course, they are really just the beginning of the systemic response. While it seems fairly clear that popularity is enormously significant, this significance still must be translated into connection through datafication and then programming. Logically speaking, translation is only likely to exaggerate the initial differences but that has not yet been demonstrated empirically.

Agency and Assemblage Measures like persistence, which begin the temporal synchronisation of signification across different perspectives, hopefully demonstrate that the required work is at least possible. If we assess meaning as the product of shifting attention within the system, then we need to know both what meanings are being contested and how different perspectives are interacting with the system around these meanings. Temporal synchronisation delivers this information and prepares it for logical interpretation. Our observations of logical processes unfolding, taken in association with the record of macro-level semiotic change, form a basis for us to assess questions like the one posed earlier. The example I have provided since is clearly too simple to allow us to say anything interesting about the influence of Twitter on the discourse, but it serves to illustrate some basic principles. What matters to the system is how it interprets differences

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between perspectives. That interpretation, and how it is processed logically, establishes precedents that ultimately shape the production of meaning. First, semiotic change happens only when new discourse events are recognised by the system, so rapid change is clearly more likely when there are more interactions. That suggests that discourse communities involving multiple user perspectives are likely to be more intense or polarising discussion spaces. Second, if interactions involve popular accounts then the influence on system discourse is exaggerated. This implies that high interaction rates within dense networks of influential users are the conditions most likely to produced rapid semiotic change. None of this should be particularly surprising. What is more interesting, perhaps, is the potential for interaction between signification and the system, between particular signs and specific logics. For instance, could more extreme signifiers be more likely to be rewarded by the system—both independently and interactively—regardless of the individual users, networks and publics engaged in discourse? There is nothing in the theoretical principles of system interactionism that would necessarily preclude this from happening. Moreover, the logics of both the digital system and the political system are largely able to function without human input. We might argue that politics still requires humans to produce discourse, but we are increasingly aware that huge amounts of political signification online are generated automatically. It is increasingly difficult to differentiate between ‘real’ and fake accounts on multiple social media platforms and bots are already hugely influential in driving traffic and shaping productivity within automated systems. Might we be approaching the time when the digitalpolitical system forgets that it is meant to be a human construct? With such questions we approach the realm of speculative technological theory, futurism and science fiction. I don’t intend to engage with such ideas as I conclude this book. A system that functions independently from humans must still interact with them in some way in order to exert influence over them. Any such interaction is likely to produce sufficient complexity to confound any predictions I might make. Rather, I want to emphasise the self-propelling logics of systems that engage human beings but that humans might find increasingly difficult to control. This is the ultimate threat of hyperreality, I think—we lose sight of that which should be ‘authentic’, but also our judgements concerning authenticity (or morality) are increasingly irrelevant to the logical processes of differentiation that the system defines.

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Notes 1. It’s easy to forget quite how young the age of mass media still is and to assume that there was always a national public sphere in the US or the UK, through which civil society and the media interacted to hold politicians accountable, but that’s not true, of course. The mass media is a twentieth-­ century phenomenon and, before that, the politics of truth and scandal operated very differently indeed. 2. Clearly, astroturfing and bots play a significant role in these communities and, while I am not focussed on these factors here, like so many studies since ours, we noted the pervasive and malign influence of these actors. 3. This point requires a little unpacking. An API typically makes a digital system available for study in a way that it otherwise would not be. Scraping is the only other common tool for capturing data from websites. Scraping works by ‘reading’ HTML from a webpage through a browser and downloading certain parts of the page as text. Scraping is a very useful tool, but it can only capture information on the mark-up language that is presented to the browser. That means that it’s great for saving the text of comments responding to a news article, or the posts in a forum, but tells us nothing about the interactions that are happening ‘behind the scenes’ to assemble that text and arrange it on the webpage. Most APIs will return some information on background interactions happening within the system, which is where this sense of greater (if not total) system access comes from. 4. However, I must admit that this approach—which I have pursued throughout my life with objects mechanical and electrical—can sometimes conclude with very little insight gained and many things broken on the way. 5. See, for instance Westberg and Årman (2019) for an analysis of the textuality of far right extremism in Sweden, or the Rand Corporation’s Violent Extremism Evaluation Measurement (VEEM) Framework, at https://www. rand.org/randeurope/research/projects/violent-extremism-evaluationmeasurement-framework-veem.html 6. The distinction here is subtle. Essentially we are aligning both the observer and the observed system in their logical responses to interactions between them.

References Bieber, F. (2018). Is nationalism on the rise? Assessing global trends. Ethnopolitics, 17(5), 519–540. https://doi.org/10.1080/17449057.2018.1532633 Bruns, A., & Moe, H. (2014). Structural layers of communication on twitter. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and society (pp. 15–28). New York: Peter Lang.

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Conway, M. (2017). Determining the role of the internet in violent extremism and terrorism: Six suggestions for progressing research. Studies in Conflict & Terrorism, 40(1), 77–98. Driscoll, K., & Walker, S. (2014). Working within a black box: Transparency in the collection and production of big twitter data. International Journal of Communication, 8(2014), 1745–1764. Fuchs, C. (2018). Nationalism 2.0: The making of Brexit on social media. Retrieved from https://www.plutobooks.com/9781786802996/nationalism-2-0/ Gessen, M. (2018, July 17). How Putin and Trump Each Lied in Helsinki. The New  Yorker. Retrieved from https://www.newyorker.com/news/our-columnists/how-putin-and-trump-each-lied-in-helsinki Habermas, J. (2006). Political communication in media society: Does democracy still enjoy an epistemic dimension? The Impact of Normative Theory on Empirical Research. Communication Theory, 16, 411–426. Jones, C., Trott, V., & Wright, S. (2019). Sluts and soyboys: MGTOW and the production of misogynistic online harassment. New Media & Society, 1461444819887141. https://doi.org/10.1177/1461444819887141 Kakutani, M. (2018, July 14). The death of truth: How we gave up on facts and ended up with Trump, Online The Guardian. Retrieved from https://www. theguardian.com/books/2018/jul/14/the-death-of-truth-how-we-gave-upon-facts-and-ended-up-with-trump Katz, E., & Lazarsfeld, P. (1955). Personal influence; the part played by people in the flow of mass communications. Glencoe, IL: Free Press. Lewis, J. (2015). Media, culture and human violence: From savage lovers to violent complexity. London/New York: Rowman & Littlefield. Lewis, J., Pond, P., Cameron, R., & Lewis, B. (2019). Social cohesion, twitter and far-right politics in Australia: Diversity in the democratic mediasphere. European Journal of Cultural Studies, 22(5–6), 958–978. Luce, E. (2019, May 24). The global advance of ethno-nationalism. The Financial Times. Retrieved from https://www.ft.com/content/0c6e40ec-7dcc-11e981d2-f785092ab560 Midlarsky, M. (2011). Origins of political extremism: Mass violence in the twentieth century and beyond. Cambridge, UK: Cambridge University Press. Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose. arXiv. Retrieved from http://arxiv.org/abs/1306.5204 Pond, P. (2020). An event-based model for studying network time empirically in digital media systems. New Media & Society. https://doi. org/10.1177/1461444820911711

246 

P. POND

Pond, P., & Lewis, J. (2019). Riots and twitter: Connective politics, social media and framing discourses in the digital public sphere. Information, Communication & Society, 22(2), 213–231. https://doi.org/10.108 0/1369118x.2017.1366539 Westberg, G., & Årman, H. (2019). Common sense as extremism: The multi-­ semiotics of contemporary national socialism. Critical Discourse Studies, 16(5), 549–568. https://doi.org/10.1080/17405904.2019.1624183

Index1

NUMBERS AND SYMBOLS 4Chan, 223 8Chan, 223 A Abbate, Janet, 135, 149, 150 Actor network theory (ANT), 34, 52, 67–70, 80, 107, 167 Advanced Research Projects Agency (US) (ARPA), 146, 148, 149 Affects, 80, 133, 153–157, 176, 239 Affordances interactivity, 156 theory of, 155, 156, 198 Always becoming, 54, 55, 58, 59, 102n8, 107, 115, 152, 188n5 Analytical reference frames, 77, 90, 109 self-selecting, 77, 90 Anderson, Benedict, 12, 176 Arab Spring, 167 impact of social media, 168

Aristotle, 53, 118 ARPANET, 18, 144, 148, 149, 151, 152, 201 Assemblages fuzzy, 81 interactive, 34, 78, 81 Australia political mediatisation, 182 poor climate record, 182 Automation, 39, 43, 46, 47, 102n5, 140, 144–147, 149, 151, 152, 156–159, 165, 194, 196, 209 Autopoiesis externalism, 93 role of communication, 93 B Babbage, Charles, 147 Difference Engine, 147 Barthes, Roland, 97, 174, 175 Baudrillard, Jean, 13–15, 44, 45, 188n6, 213, 214

 Note: Page numbers followed by ‘n’ refer to notes.

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INDEX

Bayes, Thomas, 122, 128, 230, 231, 241 Bayesian methodology, 241 Bell Laboratories, 147 Bergson, Henri, 112, 115, 118, 119 view of time, 112, 118 Berners-Lee, Tim, 150, 200, 202 Big data, 42, 160n4, 166 Bitchute, 223 Bolsanaro, Jair, 223 boyd, danah, 22, 140, 147 Brexit, 11, 181, 182, 213, 223 Vote Leave campaign, 11 Bridgman, Percy, 114, 115, 122, 124, 129, 214n2 Broadcast media, 6, 140 See also Digital media; Mass media Brown, George Spencer, 82 Bruns, Axel, 29, 152, 210, 211, 215n6, 227 Bush, Vannevar, 200 memex, 200 Butterfly effect, 89, 105 C Cable news, 14, 179, 183–185, 213 Camera, 4, 7, 8, 12, 108, 109, 113 impact on Victorian society, 12 Cartesian subject, 95 Castells, Manuel, 11, 48n3, 141, 143, 148, 182 CERN, 150 Chaos theory, 32, 61 Chomskey, Noam, 176 Circular reasoning, 110 Communication communicative action, 48, 168–170, 172, 173 communicative complexity, 172, 174 communicative logics, 98, 188n2 as mechanical process, 99

Complexity mediatised product, 15 ordered, 62, 63 product of digitised informationalism, 15 systems approach, 73 uncertainty, 19, 24, 33, 45, 112, 126 Computer code, 199 Connectivity, 22, 39, 44, 46, 47, 88, 139, 142–145, 157, 165, 199, 207 Connotation, 97, 174, 197 Context, 6–8, 17–24, 32, 34, 36, 39, 61, 62, 78, 79, 90, 93, 97, 108–111, 121, 128–130, 135, 141, 153, 156, 166, 169, 188n4, 195, 204, 215n7, 221–223, 228, 231, 238, 239 Covington schoolboy videos, 7 Crawford, Kate, 140, 147 Cultural algorithms, 206 Cybernetic theory, 79 D Daesin, 115 Datafication, 22, 46, 88, 102n5, 139–141, 143, 145, 157, 165, 194, 199, 242 De Saussure, Ferdinand, 97, 188n4 DeLanda, Manuel, 64–66 Deleuze, Gilles, 34, 64, 65, 67, 68, 96, 188n5 Deliberation, 38, 40, 169–174, 176, 185, 193, 220 Derrida, Jacques, 29, 197 Determinism, see Technological determinism Difference, 34–36, 47, 56–58, 78–87, 91–94, 96, 98, 101n1, 102n7, 108, 118, 134, 139, 155, 160n5, 167, 173, 174, 180, 186, 187, 206, 234, 240–242

 INDEX 

Differentiation, 15, 33–35, 38–41, 46, 77–101, 106, 110, 111, 119–126, 134–138, 143–146, 151–156, 158, 159, 170, 176, 184–187, 188n2, 188n3, 195, 196, 198, 199, 208, 209, 220, 232, 234–236, 239–241, 243 See also Functional differentiation Digital differentiation, 185, 220 Digital literacy, 156, 198 Digital media communication speed, 141 complexities, 42, 53 processes, 47, 88 See also Digital systems Digital-political system, 35, 44, 166, 243 Digital systems, 13, 38, 39, 45, 47, 99, 137, 139–145, 147–149, 151, 152, 157, 158, 165–167, 173, 184, 186, 187, 194, 195, 198, 208–212, 223, 225, 227, 228, 230, 243, 244n3 Duterte, Rodrigo, 223 E Egypt uprising, 167 impact of social media, 167 Einstein, Albert, 112–115, 118, 119, 122, 129 theory of relativity, 112, 113, 122 view of time, 112, 118, 119 Electrification, 135 social impact, 135 Empiricism, 68 Engagement metrics, 220 Entropy, 60, 101n2 Event-based ontology, 58, 59, 107, 152 Exteriority, 65, 66 Extinction Rebellion, 178

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F Facebook friendship on, 71, 72, 189n7 as global mediasphere, 158 news feed, 207 Fake news, 6, 13, 16, 179, 206–208 See also Trump, Donald Federal Networking Council (FNC), 149 Feenberg, Andrew, 39, 136 Foucault, Michel, 57 Frames, 13, 16, 17, 36, 55, 77, 78, 90, 107–111, 123, 130, 134, 135, 147, 154, 156, 158, 172, 175, 183, 198, 199, 210, 211, 221, 222, 227, 236 framing process, 108 Functional differentiation, 81, 89–91, 94, 96, 151 G Gab, 223 Genealogy, 57 General systems theory, 79, 80, 96 Gibson, James, 155 Gillmore, Dan, 169 Global media network, 12 Global mediasphere, 12, 157, 158, 179 See also Semantic mediasphere Google Scholar, 17, 37 Guattari, Félix, 64 H Haaland, Debra, 6 Habermas, Jürgen, 40, 48n2, 168–171, 220 Hall, Stuart, 97, 174 Happiness links to social media, 17–19, 21–24, 56, 63 measures of, 19, 20

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INDEX

Hartley, John, 47, 84, 93–95 Hashtags gaming data, 212 political, 208–214 semantic reduction, 212 See also Twitter Hassan, Robert, 18, 141, 147, 186, 187, 206 Heidegger, Martin, 57, 58, 60, 79, 115, 136 Heraclitus, 118 worldview of constant flux, 59 Historical materialism, 57, 74n1 Hong Kong protests, 168 impact of social media, 168 Hughes, Thomas, 135, 136 Husserl, Edmund, 79, 80, 101n1 Hyperlink, 199, 202–205 destabilisation of words, 41, 204 See also Hypertext Hyperreality, 13–16, 213, 243 Hypertext and capital, 206–208 corporate-political owners, 203 dissociative nature, 206 global media as, 199 logic of, 42, 202–206, 210 See also Hyperlink Hypertext Mark-up Language (HTML), 150, 200, 203, 244n3 Hypertext Transfer Protocol (HTTP), 150 I Ideal speech conditions, 170, 173 Indigenous Peoples March, 5, 8 Information overload, 15, 173 Instagram, 3, 16, 22, 23, 31, 47, 141, 157, 165, 166, 194, 224 Instrumentalization theory, 136

Interaction event, 35, 62, 64, 73, 77, 81, 82, 90, 95, 98, 100, 106, 109, 119, 121, 124, 128, 153, 155, 166, 186, 187, 193, 195, 197, 210, 212, 221, 226, 229, 232, 238 filtration, 107–111 Interactionism, 24, 33, 34, 37, 42–45, 52–54, 58, 59, 63, 65–68, 77, 79–81, 83–85, 87, 91, 95, 96, 99, 101n1, 106–109, 115, 127, 129, 130n1, 166, 167, 197, 198, 214, 220–222, 225, 234, 243 See also Systems Interiority, 65, 66 Internet action-oriented models, 168 as Cold War technology, 135 layered software technology, 153 speed of information, 172, 173 Iran protests, 167 impact of social media, 167 J Jacobs, Jane M., 66 Jacobsen, Thomas L., 169, 170 Johnson, Boris, 223 Joyce, Michael, 41, 202 JSON, 196 K King, Martin Luther, 2 L Language games, 97, 188n4 Language wars, 175, 176 Langues, 97, 188n4 Latour, Bruno, 67–70, 81, 96, 107, 108, 167 Legal system, 60, 90, 92, 93

 INDEX 

Lewis, Jeff, 10, 40, 43, 97, 110, 142, 157, 174–176, 188n4, 214n1, 219, 222 Lincoln Memorial, 1, 2, 6, 9, 25n1, 30, 129 Literacy, 153 See also Digital literacy Logical interactive potential, 155 Logical media systems, 157–159 Logics, 15, 32, 51, 78, 109, 133, 157–159, 165, 193, 202–206, 220 Lorenz, Edward, 89 Luhmann, Nicklas, 34, 38, 79–87, 90–99, 101n1, 102n6, 102n8, 109, 139, 155, 186, 195, 199 Luther, Martin, 176 M Macro system theory, 90 MAGA-world, 13–16 Manovich, Lev, 150, 199, 206 March for Life, 3, 5 Marx, Karl, 57, 74n1, 111 Mass interaction, 151 Mass media, 5, 22, 29, 38, 43, 48n3, 91, 138, 139, 141, 142, 175, 183, 184, 244n1 Materialism, 57, 74n1, 111 Maxwell’s equations, 73 McLuhan, Marshall, 134, 135 Mead, George Herbert, 43, 113–115, 214n2, 236 Media broken faith in, 184 news cycle, 183 political spin, 183 system-sign relationship, 184 See also Broadcast media; Digital media; Mass media; Media technology; Media markets; Mediatisation Media markets, 22, 180

251

Media technology, 9, 15, 16, 18, 32, 33, 46, 134, 135, 138, 145, 160n2, 176, 193 See also Technological determinism Mediatisation, 177–184 Mediaworld, 14, 51, 52 Modi, Narendra, 223 Moe, Hallvard, 152, 210, 211, 227 Morrison, Scott, 182, 188n5, 223 N Nagel, Rebecca, 9 Nationalism, 12, 181, 182, 223, 237, 239 ethno-nationalist discourses, 222, 226, 237, 239 Nature-culture synergy, 65 Nelson, Ted, 201, 202 Neoliberalism, 166, 175, 209 Network culture, 138 Networking, 11, 39, 43, 46, 47, 141, 144–148, 151, 152, 156, 158, 159, 165, 187, 188n2, 193, 194, 209 Networks, 12, 34, 36, 42, 51, 56, 59–62, 66–74, 81, 84, 88, 91, 96, 143, 145, 148–151, 155, 157, 168, 172, 185, 188n2, 196, 201–204, 212, 228, 229, 241, 243 See also Social networks Newton, Isaac, 73, 118, 235 O Objects, 13, 21, 29, 34, 42, 45, 46, 53–58, 67–69, 73, 80, 81, 84, 112, 113, 115, 117, 119, 124, 130n1, 133, 134, 136, 143, 151, 153, 155, 156, 186, 205, 209, 215n4, 244n4 simplicity of, 53, 54 See also Things

252 

INDEX

Observation, 18, 30, 35, 36, 39, 44, 52, 57, 63, 67–69, 77, 81, 84, 88, 94, 97, 99, 100, 106–109, 112–114, 116–120, 122–125, 128–130, 130n2, 152–154, 166, 167, 194, 196, 212, 214n2, 224, 225, 233, 234, 236, 242 temporalisation of, 108 Orbán, Viktor, 12, 223 Orwell, George 1984, 177 P Packet switching, 144, 148 Pan, Lingling, 170 Pence, Mike, 3 Perlstein, Rick, 11, 208 Perspective implications of, 35, 107, 115 productive, 108 reality as construct of, 13, 106, 113, 114, 229 Perspectivism complexity, 33, 43, 121 multiplicity of perspectives, 121 Phenomenology, 34, 57, 59, 113, 115, 206 Phillips, Nathan, 5, 8, 10, 30, 31, 194 Place, 1–3, 8, 10, 14, 15, 40, 46, 54, 60, 62, 81, 84, 89, 96, 112, 113, 116–118, 138, 146, 151–153, 169, 176, 180, 188n6, 193, 195, 197, 198, 201, 203, 214n2, 216n9, 223, 226 performative nature, 14, 189n7 Plato, 53, 118 Poell, Thomas, 22, 38, 47, 88, 138–142, 157, 165 Polarisation of media, 220, 222, 223 of politics, 29, 44

Political public, 33, 128, 165–187, 195, 208, 210, 220, 222 Political system interaction with digital system, 36, 39, 44, 111, 166, 194, 209, 210, 220 logics, 40, 44, 171, 194, 243 meaning making, 35 polarisation, 220–222 Politics analogue, 184–186 mediatisation, 177, 182, 184 as performance, 14, 182 as violence, 175, 188n6 See also Political system Pond, Philip, 10, 42, 43, 115, 118, 142, 152, 155, 156, 168, 173, 205, 213, 222, 226, 229, 241 Popularity, 22, 44, 46, 47, 72, 88, 125, 139, 141–145, 157, 165, 166, 171, 185, 187, 194, 199, 207–209, 212, 241, 242 Potts, Jason, 84, 93–95 Price, Huw, 100 Printing press, 13, 176, 177 Probability theory, 124 Process, 2, 8, 11–15, 20, 30, 33, 34, 36, 38, 41, 42, 44, 45, 47, 52–54, 58, 60, 61, 64–66, 74, 77, 78, 80, 84–88, 93, 94, 97–100, 105, 108–111, 117, 119–121, 125, 128, 133, 138–141, 143, 146, 147, 152–157, 168, 169, 172–175, 181, 183, 185–187, 194–197, 199, 202, 205, 208, 210, 212, 221, 227, 229, 231–233, 239–243 as event chain, 86 Programmability, 22, 46, 88, 139, 140, 143–145, 157, 165, 194, 199, 207

 INDEX 

Propaganda campaigns, 176 Public sphere, 2, 11, 15, 168, 171, 173, 177, 182, 244n1 mediatisation of, 182 Putin, Vladimir, 223 Q Quantum physics/mechanics, 34, 53, 57, 58, 73, 112, 114, 122, 130n1, 198 Quantum time, 100 R Radar, 147 Railroad, 12, 17 impact on Victorian society, 12 RAND Corporation, 147, 244n5 Raytheon, 147 Reality objective, 30, 35, 113, 114, 130n2, 196 as perspectival construct, 114 sense of shared, 12, 178 Reddit, 157, 223, 231 Rhizomatic theory, 66 Roe v. Wade, 3 Roth, David, 179 Rovelli, Carlo, 30, 55, 56, 58, 65, 73, 98, 101n2 Russia, 182 lack of independent media, 183 S Self-differentiation logics, 46, 109, 111 of technology, 46, 133 Self-reference, 80, 84–89, 96, 141, 222 Semantic mediasphere, 183

253

Semiotic struggle, 38, 97, 197 Signification extreme signifiers, 228, 243 signifier-signified relationship, 204 struggle, 31 Silicon Valley, 16, 37, 102n5, 127, 137 Smolin, Lee, 30, 54, 55, 57 Social media commercialisation, 144 connectivity, 22, 44, 46, 88, 139, 142, 143, 157 datafication, 22, 46, 88, 139, 143, 157 definitions, 21–23, 137, 139, 158 impact of iPhone, 18 impact on society, 143, 244n1 links to happiness, 17–19, 21–24, 56, 63 logics, 22, 47, 88, 144–146, 157–159 measurement of use, 24 monetisation, 144, 166 popularity, 22, 46, 88, 139, 142, 143, 166 programmability, 22, 46, 88, 139, 140, 143, 145, 157 reverse engineering, 145 use by teenagers, 17, 18, 21, 44 Social networks complex, 72 one-degree, 71 Social reality as co-construction, 135 systems theory of, 51–74 Social systems theory, 79, 85, 90, 92, 195 Software studies approach, 151 Solnit, Rebecca, 12 Stability, 62, 86, 98, 139 Standing Rock reservation, 5 Subject position, 120

254 

INDEX

Symbolic performance, 14 System interactionism, 24, 33, 34, 37, 42–45, 52–54, 58, 59, 63, 66–68, 77, 79–81, 83–85, 87, 91, 95, 96, 101n1, 106–109, 115, 127, 130n1, 142, 143, 152, 155, 166, 167, 197, 198, 220–222, 225, 234, 243 as theory about objects, 53 System/network dynamics, 81 Systems based ontology, 58, 59, 152 boundaries, 55, 80, 85, 92, 94, 96, 101n2, 109 closed, 85, 92, 93, 96, 97, 107 as collections of sub-systems, 59 communication, 11, 39, 42, 77–101, 102n8, 186, 242 definition, 77, 83, 144 distribution of possible states, 57 fracturing, 42, 61 interactionism, 24, 33, 34, 37, 42–45, 52–54, 58, 59, 63, 66–68, 77, 79–81, 83–85, 87, 91, 95, 96, 101n1, 106–109, 115, 127, 130n1, 142, 143, 152, 155, 166, 167, 197, 198, 220, 221 kinetic, 86 language of, 66, 79, 83, 143, 186 movement, 60 open, 92, 93, 96, 205 physics, 55 political, 14, 36, 39, 40, 42, 47, 48, 85, 92, 93, 111, 149, 166, 168, 170, 171, 174, 176, 183, 185, 193–196, 199, 207–210, 215n7, 243 as reference frames, 55, 77, 90, 109, 110 role of environment, 42, 83, 95

self-differentiation, 109, 111, 133 symbolic, 41, 62, 77, 78, 106, 186, 195–197 systemic complexity, 63, 109, 130n2, 197 temporal, 42, 58, 77, 86, 109, 205, 238 textual interactions between, 91, 198 See also Complexity; Digital systems; General systems theory; Political system; Stability; System/network dynamics; Systems thinking Systems thinking, 57, 79, 92 T Tarde, Gabriele, 82 Techno-determinism, 172 Technological determinism, 135, 151, 154, 155, 172 Technology differentiating logics, 134 enlightenment, 137 expansion, 12, 13, 137 military sponsorship of development, 146 production by humans, 17–24 rebellion, 137 warfare, 137 Techno-philia, 171 Techno-social study, 52 Temporality, 69, 121, 205, 212, 213, 233–236 See also Time Territorialisation/ deterritorialization, 66 Things vs events, 58 world of, 54, 59, 63, 112

 INDEX 

Time objective, 68, 118, 119 subjective, 115, 118, 119 Transistor technology, 147, 148 Transmission Control Protocol/ Internet Protocol (TCP/IP), 149 Trump, Donald aggressive conservatism, 5 ‘drain the swamp’ mantra, 179, 180 first hyperreal politician, 184 government shutdown, 3 impeachment, 180, 208 invented reality, 11, 14 Make America Great Again (MAGA) cap, 3, 13, 31, 180 rhetoric, 177, 185 spectacle of, 14, 180, 184, 185 Trust crisis of, 9–13 in media, 9, 10, 12, 13 as tribal and exclusionary, 10 Truth, Trust and Technology Commission (UK), 10 Turing, Alan, 68, 146, 147 Turing machine, 136, 144, 151, 159, 187 Turkey, 183 lack of independent media, 183 Turkle, Sherry, 171, 206 TV cathode-ray tubes, 73 Twitter algorithms, 88, 196 application programming interface (API), 211, 224, 225, 227–229, 237 hashtags, 166, 196, 204, 210, 211 internal system dynamics, 125 retweeting, 125

255

U Uighurs, 11 Understanding Society, UK Household Longitudinal Survey, 20 Uniform Resource Identifier (URI), 150 Unifying logic, 59, 62, 63, 78 V Validity claims, 170, 173 Van Dijck, José, 22, 38, 47, 88, 138–142, 157, 165 Vietnam War, 2 Von Bertalanffy, Ludwig, 79, 96 W War on terror, 183 Whitehead, Alfred North, 115 Wiener, Norbert, 79 Williams, Raymond, 135 Wittgenstein, Ludwig, 97, 188n4 World Wide Web (WWW), 47, 70, 150 World Wide Web Consortium (W3C), 200 X Xanadu, 201, 202 Y YouTube, 5, 6, 31, 47, 142, 157, 167, 223, 224 Z Žižek, Slavoj, 176 Zuckerberg, Mark, 203