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The Hybrid Media SystemPolitics and Power$

Andrew Chadwick

Print publication date: 2017

Print ISBN-13: 9780190696726

Published to Oxford Scholarship Online: August 2017

DOI: 10.1093/oso/9780190696726.001.0001

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Donald Trump, the 2016 U.S. Presidential Campaign, and the Intensification of the Hybrid Media System

Donald Trump, the 2016 U.S. Presidential Campaign, and the Intensification of the Hybrid Media System

(p.240) 10 Donald Trump, the 2016 U.S. Presidential Campaign, and the Intensification of the Hybrid Media System
The Hybrid Media System

Andrew Chadwick

Oxford University Press

Abstract and Keywords

Chapter 10 extends the conceptual framework to the extraordinary 2016 U.S. election, showing how Donald Trump’s rise and Hillary Clinton’s downfall were enabled by key aspects of the hybrid media system. The chapter deciphers the main components of Trump’s digital campaign, in particular its shift toward an intensive Facebook advertising strategy and its use of targeted advertising to try to reduce turnout among potential Democrat voters. It shows how Trump was able to translate his celebrity capital into political capital through the use of social media, particularly Twitter, to influence press and television coverage. Chapter 10 also discusses how hybrid media played a decisive role in the Women’s March, the biggest single-day protest in U.S. history. The march, when integrated with the actions of professional fact-checking journalists, became an important part of the counter-inauguration that subverted Trump’s ability to set the agenda during his first week in office.

Keywords:   Donald Trump, election, protest, social media, Twitter, Facebook, advertising, Women’s March

Washington, DC, January 20–22, 2017: Presidential Inauguration Weekend

For Duff Goldman, there is a problem with the cake.

At first glance, it is difficult to see why. It is an extraordinary creation—a four-feet-tall, nine-tiered, teetering panoply of frosting in red, white, blue, and silver. On its side, lovingly rendered in food coloring, is a faithful reproduction of the presidential seal. At its peak, suspended on slender sticks, are several silver-coated, cookie-dough stars that appear to float in mid-air.

This is no ordinary cake. It is for a Washington party—the Salute to Our Armed Services Ball—following the inauguration of Donald J. Trump as president of the United States (Wang & Carman, 2017). Trump and Vice President Mike Pence were the first to cut into the cake, not with a knife, but an antique military sword. The Washington Post recorded video of the occasion and posted the file to its website, marking an event that would immediately resonate with the many who have witnessed similar, if less grandiose, symbolic gestures at their own family celebrations.

But Duff Goldman has a problem. This cake is identical to one he designed, baked, and decorated four years earlier. Then, the occasion was equally important: the 2013 inauguration ceremony for President Barack Obama. Had Goldman been invited back to the White House to recreate his masterpiece for Trump? No. In fact, Goldman had nothing whatsoever to do with Trump’s inauguration cake. Someone else had made it, copying Goldman’s 2013 design down to the last detail. We know this because Goldman tweeted his distaste for the copy of his cake. And, within a few hours, the story of the fake cake had traveled around the globe.

Duff Goldman is a pastry chef and minor celebrity who made his name on a Food Network television show, Ace of Cakes, featuring daily life at his Baltimore bakery. (p.241) As of January 2017, about 115,000 people followed him on Twitter. Late into the night of inauguration day, frustrated upon seeing the replica of his cake online, Goldman posted a photo showing Trump’s cake alongside a photo of the cake he had made for the Obama White House in 2013. He wrote: “The cake on the left is the one I made for President Obama’s inauguration 4 years ago. The one on the right is Trumps [sic]. I didn’t make it.” The photos showed the two cakes to be almost identical, though, unsurprisingly, Trump’s was slightly bigger. Goldman rounded off his tweet with the emoji symbol for what can best be described as a thinking-while-stroking-your-chin face—perfect for conveying sarcasm: “Hmm . . . I wonder what happened there . . .” (Goldman, 2017).

Then, Goldman’s tweet went viral. By the following evening, it had received more than 125,000 retweets, had been “liked” by more than quarter of a million people, and had attracted more than 5,000 replies. Many of those engaging with the tweet added the hashtag #cakegate to their own messages. And by Sunday evening, professional media organizations around the world, including the Washington Post, USA Today, the New York Daily News, the Chicago Tribune, and Britain’s Telegraph and Guardian, to name just a few, were running articles about how Donald Trump’s inaugural cake was a “plagiarized” confection. Journalists managed to trace the cake back to Buttercream Bakeshop, a Washington business that had been commissioned by White House staff. The source for this information? An Instagram post from Buttercream, making it clear that the company was “asked to replicate someone else’s work” (Wang & Carman, 2017). By Monday morning, my search for the exact phrase “Trump inauguration cake” on Google News revealed that the story had already been woven into the fabric of the web: more than 141,000 pages now contained this most unlikely combination of words.

Duff Goldman followed up with magnanimous tweets expressing support for the company asked to copy his design. Then, in response to criticism from members of the public on Twitter and Instagram, Buttercream Bakery announced that the profits from their work for Trump would go to the Human Rights Campaign, a well-known civil society organization that campaigns for equality for LGBT people (Wang & Carman, 2017). All appears to have ended well in the world of high-specification catering.

But to understand the broader significance of Duff Goldman’s viral tweet, we need to consider the systemic context that enabled its production and circulation. In particular, we need to understand the proximate webs of meaning that gave the tweet such powerful resonance among social media users and professional journalists alike. This chain of events was simultaneously extraordinary and predictable. It was extraordinary because it demonstrates how social media have reshaped the mediation of politics. It was predictable because it was simply the latest episode in a 2016 presidential election whose mediation was fundamentally and consequentially hybrid in its integration of older and newer technologies, genres, norms, behaviors, and organizational forms.

(p.242) For Donald Trump’s press team, things were certainly not all well with how the inauguration celebrations were being framed and reported—both by professional journalists and many members of the public on Twitter, Facebook, and Instagram. The fake cake became a politically charged, memetic metaphor for Trump, the man, and his character. The tweets, retweets, and replies of #cakegate enabled many to assert and galvanize their political identity by using the cake as a symbol for all that liberals had found unappealing about Trump since he had begun his campaign for the White House in 2015—the empty glitz, the vulgarity, the lack of grace. The fake cake became emblematic of Trump as chancer: the one who had gone a long way on very little, and who was prepared to stoop so low as to rip off a baker, of all people. This was a confluence of media event, minor celebrity, Twitter, Instagram, professional journalists eager for a fresh angle on the new presidency, and liberal social media users eager to position the cake as a culturally charged intervention in the flow of symbols that might make a difference to the reporting of inauguration weekend.

The fake cake was all the more powerful because it involved a mobilization against Trump of symbolic resources from the field of lifestyle celebrity culture. This was, after all, a field in which Trump had long been prominent and which had been crucial for his rise through the ranks of American public life. This was about a more authentic, humble, down-to-earth, everyday form of celebrity—the minor celebrity baker defending his livelihood against Trump’s narcisissm. It spoke to the importance, in everyone’s lives, of the art of the celebratory event: of wedding cakes, christening cakes, Bar Mitzvah cakes, and all the rest; events when we make an effort to do the right thing, get things just right, put on a bit of a show, dress up, perhaps gather in a fancy hotel, to show how much we care about family and friends. In late modernity, a constellation of media practices surrounds such personal family events, adding to their intensity. These events’ ceremonial character is as much influenced by the genres of reality television, instructional YouTube videos, online lifestyle platforms like Pinterest, and the social media–enabled sharing of advice, tips, and emotional support, as it is by traditional and religious customs. The event industry is big business, and Trump is an important part of it. He is chief of a leisure empire on which he built his reputation with hotels, resorts, golf courses, wineries, fragrances, clothing, jewelry, home furnishings, chocolate, and vodka, among other things, and which he ceaselessly cross-promoted through his celebrity persona on the television show The Apprentice. This is why #cakegate resonated with hundreds of thousands of Twitter users and the professional journalists who remediated the story of Duff Goldman’s problem.

For Sean Spicer, there is a problem with the size of the crowd.

It is easy to see why. Spicer is Donald Trump’s new White House press secretary. Earlier that day, his boss, President-elect Donald Trump, walked out of the Capitol building to be formally inaugurated as 45th president of the United States. Trump (p.243) delivered an uncompromising, combative speech that echoed all of the themes of his election campaign. The event was broadcast, web streamed, tweeted, Facebooked, Instagrammed, and Snapchatted live across America and far beyond. It was a day of celebration for Trump, his family, and his staff, including Spicer.

But as Trump had emerged from the Capitol building, on an observation platform close to the top of the famous stone “needle” that is the Washington Monument, sat Lucas Jackson, 500 feet above the ground, and breathless from climbing fifty flights of stairs. Jackson is a well-known photojournalist for Reuters, the highly respected, second oldest, second largest news agency in the world.

Jackson’s job was to point his camera east toward the Capitol and take photos of the crowd watching Trump. At 12:01 p.m., with Trump on the stage, Jackson took his picture and uploaded it to Reuters’ office. Editorial staff at the agency then pulled out an old photo of Obama’s 2009 inauguration and compared it with Jackson’s. The differences were stark and newsworthy. At 2:02 p.m. Reuters published on their website a composite image showing the two crowd photos side by side. The Obama inauguration photo showed crowds almost completely filling the National Mall, stretching all the way back to the National Monument and spilling over into Madison and Jefferson Drives. The Trump photo showed that the crowd started to thin out about halfway down the Mall and did not reach the Monument. The difference between these photos potentially went to the heart of the debate about one of Trump’s obvious characteristics during the 2016 presidential campaign: his desire to be portrayed as popular—a winner. Reuters’ message was clear: Trump could not draw a crowd like Obama could.

We know all this because Reuters later published a self-congratulatory article, describing how Lucas Jackson had got his shot (Trotta, 2017). Significantly, however, Reuters’ piece showing the two photos had made no comment on the size of the crowds. It did not need to contain these details because, as soon as the article was posted, it went viral on Facebook and Twitter, as hundreds of thousands recirculated the image, adding their own commentary about how it revealed Trump’s relative unpopularity. Within hours, the major US news organizations, including CNN, CBS, the Washington Post, the New York Times, and USA Today, to name just a few, had picked up the images and published their own stories. This was Sean Spicer’s problem.

Spicer was the incoming White House press secretary for the Trump administration. It was his first day in the job, and professional media framing of the inauguration was slipping through his fingers. Two forces were working against him: professional news agency Reuters’ access to the best spot on the National Mall, secured through their status, reputation, and their network of contacts in the US capital; and the actions of hundreds of thousands of liberal activists eager to criticize Trump on social media platforms. Presidential inaugurations usually see the incoming administration receive a light touch from journalists, but not on this occasion. From Spicer’s point of view, this required a response.

(p.244) Like the story of Duff Goldman and the fake cake, what happened next reveals key aspects of the hybrid media system in flow. In particular, it provides further evidence for the theory of the political information cycle that I advanced in chapter 4. For Reuters’ journalism and the distributed social media commentary that it sparked were soon joined by a third force: the spectacle of a mass, online, coordinated protest in physical space, not only on the same hallowed ground of the National Mall, but also around the world. This protest—a global event, but also, as we shall see, an event that would have particular resonance in the nation’s capital, would seriously undermine Spicer’s ability to frame inauguration day as a great celebration of the Trump presidency.

In the weeks leading up to the Trump inauguration, a wide range of women’s and progressive civil society groups across the United States and many other countries had been planning what would become known as the Women’s March. The movement began on Facebook, the day after the November election, when a Hawaii resident, Teresa Shook, created an event page calling for a women’s rights march to protest against Trump for his widely reported misogynistic remarks during the campaign. Shook invited a few of her online friends to sign up. The following morning, she awoke to find that the link to the event had been distributed to women’s groups online, including Pantsuit Nation, a Facebook group that had been established to galvanize women’s support for 2016 Democratic presidential candidate Hillary Clinton. Shook reported that, within a day of her post, the Facebook event page “went ballistic” (Kearney, 2016). These developments went largely unnoticed at the time, but Shook’s remark proved to be a fair assessment of what was to come.

As word of the planned protest spread online, a large coalition of organizations began to grow around it, including the AFL-CIO, Greenpeace, the National Organization for Women, Amnesty International, Planned Parenthood, and a range of much looser online movements such as 350.org, Hollaback!, and Free the Nipple. The coalition, which also attracted a range of celebrities including actors America Ferrera, Ashley Judd, and Scarlett Johansson, among many others, planned a coordinated march in multiple cities across the United States and worldwide.

By the week before inauguration weekend, there were more than 400 “partner” groupings listed on the Women’s March website. Underway was a plan to knit hundreds of thousands of what the organizers called “pussy hats”: bright pink winter caps with pointy “ears” that would immediately identify the protestors in photos and video of the event. The pussy hats mischievously referenced an extraordinary Washington Post article that had been published just weeks before election day. The piece had revealed archival video out-takes that showed how, in an interview with a television presenter, Trump had once bragged that, such was his fame, women would supposedly allow him to “grab them by the pussy” (Farenthold, 2016). The Pussy Hat Project (https://www.pussyhatproject.com) was the latest in a long line of movement repertoires that have built solidarity by defiantly reclaiming and (p.245) reinventing a (sometimes) derogatory term. It started to look like the Women’s March might be a significant event involving large numbers, both in the United States and around the globe (Women’s March, 2017). The date was set for January 21, the day after the presidential Inauguration. The main U.S. march was to take place on the National Mall.

That same day, January 21, Donald Trump was scheduled to make a speech to staff at CIA headquarters in Langley, Virginia, just outside Washington. Overnight, he had discussed media reports of the size of the inauguration crowd with Sean Spicer (Prokop, 2017). Trump had instructed Spicer to convene a White House press briefing to deal with the issue. The goal was to present the crowd photos as misinformation and fabrication by hostile journalists. To prime the news agenda while speaking to the CIA, Trump pointedly remarked on the size of his inauguration crowds and went into considerable digressive detail about how journalists had got it all wrong. Trump claimed that there were a “million, million and a half people” on the Mall (Lee, 2017). The scene was set for Spicer’s press briefing later that afternoon.

But as Trump was speaking to the CIA, Women’s March protestors were already flooding the National Mall and the surrounding streets, for the Washington node of a global event. The march as a whole turned out to be the largest single-day protest in U.S. history. An estimated 4.8 million people took part, 3.5 million of them in the United States (Tufecki, 2017).1 In Washington alone, an estimated 470,000 took to the streets (Wallace & Parlapiano, 2017). Marches took place in all major U.S. cities, with particularly large crowds in Chicago, Los Angeles, New York City, and Seattle, though many smaller cities and towns across the country saw protests as well. Marches also happened in eighty-one other countries. Just as significant were the huge volumes of social media posts about the marches. Many of these included photos of the crowded Washington subway and streets and drew direct comparisons with the size of the crowds for the previous day’s inauguration. And these were remediated in articles that aggregated and embedded the tweets on high-traffic news sites, including Buzzfeed (Gallucci, 2017; Reinstein, 2017).

Later that afternoon, Spicer gave the very first White House press briefing of the Trump era. Before a room of elite reporters from U.S. media organizations, he accused journalists of “deliberately false reporting.” He proceeded to read out a five-minute statement that disputed the veracity of the photos of the crowds on the National Mall. He argued that media organizations were aiming to “lessen the enthusiasm of the inauguration” by downplaying what he said was the real size of the inauguration crowd. He mobilized a range of evidence for his case—that the floor coverings that were used “for the first time in our nation’s history” to cover the lawned areas had “the effect of highlighting any areas where people were not standing” and that the DC Metro had reported “420,000 people using public transit,” compared with only “317,000” who had used it for Obama’s 2013 inauguration. Spicer concluded that “[t]‌his was the largest audience to ever witness an inauguration—period—both in (p.246) person and around the globe” (Kessler, 2017). Without taking questions and without mentioning the Women’s March, Spicer walked out of the room.

Spicer’s press conference came at the end of a long campaign that saw Trump repeatedly attack professional media for their unfavorable coverage and their failure to report the size of the crowds attending his rallies (Prokop, 2017). But Spicer’s first White House event left professional media organizations shell-shocked. Here was the new administration—in its very first opportunity to engage media—accusing journalists of essentially fabricating the claim that Trump’s inauguration crowd was smaller than Obama’s, despite clear photographic evidence to the contrary.

But Spicer’s intervention was, in many respects, based on the norms of a previous era, when scripted and staged press conferences, designed for a select group of elite broadcast and newspaper journalists, were a relatively quick and easy method to reclaim the agenda during a news cycle. Indeed, such events were often used to initiate such cycles. Does this kind of action have the same purchase in a media system that is characterized by political information cycles: news-making assemblages that include many non-elite participants, who interact online to contest official news frames or advance their own frames, in real-time exchanges?

Journalists soon began to use Twitter to spread the news about Spicer’s accusations that they had under-reported the size of the inauguration crowds. CNN and CBS, for example, uploaded video of the press conference and embedded short clips in their Twitter streams (CBS News, 2017; CNN Politics, 2017).

Meanwhile, the first reports of the global Women’s March were appearing on news organization websites and television. The crowds were clearly enormous. Spicer’s problem had not gone away. Indeed, his attempt to solve it had made things worse. Photographs and videos of the Washington march, broadcast on cable news channels, embedded on channel websites and YouTube, and pushed out via social media accounts, revealed that the protests had attracted greater numbers than the inauguration. The stage was set for a mediated struggle between the new administration and its opponents. The size of these three crowds—Obama’s in 2009, Trump’s in 2017, and those for the Women’s March—in other words, the extent of the physical, embodied opposition to Trump in proximate form and recorded historical memory, became the central point of contestation. This was a counter-inauguration.

The Women’s March was a tangible, visible, and literally massive manifestation of the opposition to Trump. In this sense it played a role similar to the huge rallies organized by Obama’s staff during the 2008 presidential campaign, which were aimed at augmenting the social solidarity of supporters previously only united by their online social network ties, and demonstrating a show of strength to both local and national media (see chapter 7). Like the story of the fake cake, the struggle to define the meaning and significance of the 2017 inauguration crowd played out through a process best described as memetic metaphor-claiming. Who owned the (p.247) right to draw conclusions about the size and meaning of the crowds? This process involved multiple actors, interacting with each other in interdependent power relations in an assemblage of older and newer media, and in full view of the public. Trump and his staff clearly saw this as a high-stakes encounter that might set the tone for their opening weeks in office. Their task was given greater urgency by the new president’s approval ratings, which, at 42 percent, were the lowest ever recorded for an incoming president (RealClearPolitics, 2017).

Next, reeling from their treatment at Spicer’s press meeting, editors at the Washington Post commissioned their veteran political reporter, Glenn Kessler, who was now in charge of the paper’s Fact Checker section, to write an extended piece evaluating Spicer’s claims. As I argued in chapter 8, fact-checking has become an important form of hybrid journalism (see also Graves, 2016). It is a means by which older media have responded to the acceleration of the news cycle and the rise of multiple, alternative news producers, many of which rely on the aggregation and recirculation of material published elsewhere rather than the independent production of their own stories. However, the rapid-fire fact-checking of today is not a simple restatement of the older values of investigative journalism. Instead, it takes acceleration, hypercompetition, and the availability of a multiplicity of sources as its foundation, while trying to position a news organization as a responsive yet authoritative debunker of official claims and a driver of the news agenda. In short, fact-checking’s attraction to a cash-starved news industry is that it can now be assembled quickly and relatively cheaply, often from online fragments of information, such as tweets and charts from government reports. But it still enables journalists to become authoritative anchors in the sea of uncertainty that usually washes around a political scandal, a media event, or a decisive period of partisan conflict.

Kessler’s Fact Checker article for the Washington Post appeared on Sunday, the morning after Spicer’s press conference and two days after the inauguration (Kessler, 2017). It was an extraordinary piece of integrative journalism, notable for its fusion of traditional investigative judgment, eyewitness accounts, and expert testimony. Kessler gathered material from a variety of journalists on the ground and embedded their tweets, some of which featured photos that had been taken four years earlier. He marshaled evidence from computer scientists who had used specially developed software to estimate the size of the crowd in the images provided by Reuters. Kessler pointed out that, due to the left-leaning character of the Washington area, there was never much likelihood that Trump would draw a crowd larger than the estimated 1.8 million who turned out for Obama in 2009.

Kessler then proceeded to demolish every other claim made by Trump and Spicer. The floor coverings on the Mall were not, in fact, new but had been used in 2013 for Obama’s second inauguration. A photo taken in 2013 by CNN journalist Ashley Killough showed this to be the case. Trump’s crowd did not extend past 10th Street. Photo tweets from PBS’s Lisa Desjardins and a post from a Washington Post video (p.248) editor, Gillian Brockell, proved this. Kessler cited a New York Times article featuring UK-based computer scientists Marcel Altenburg and Keith Still, who specialize in algorithmic analysis of large crowd photography as part of their work for police and event planners (Wallace & Parlapiano, 2017). Altenburg and Still estimated that the Women’s March on the Mall had drawn three times as many people as Trump’s inauguration. Spicer’s claim that more people had used the DC Metro for Trump’s inauguration was also false. Both of Obama’s inaugurations saw larger numbers use the Metro and the 2009 event saw 1.1 million trips—the busiest single day in the DC public transport system’s history. And, in a final blow, Kessler pointed out that the second biggest day in the Metro’s history was the day of the Women’s March, when there were almost twice as many transit rides as there had been on Trump’s inauguration day.

When, later that Sunday evening, Trump’s senior adviser and campaign manager, Kellyanne Conway, appeared on NBC’s flagship political discussion show, Meet the Press, she stated that Spicer had merely used “alternative facts” to describe the inauguration. Conway’s remark was met with incredulity by the show’s presenter, Chuck Todd, who replied, “Alternative facts are not facts. They are falsehoods” (Blake, 2017). By then, it was clear that the administration’s attempt to discredit journalists had failed.

In a period of just two days, the meaning of the inauguration had shifted. Such ceremonial events usually provide a straightforward opportunity for a new administration to present itself in the best possible light. But this inauguration became highly contested and will be remembered as such for generations. How and why this came to be the case is explained by the complex interdependencies of the hybrid media system. Trump’s belligerent insistence that reporters were fabricating evidence to discredit his reputation met with resistance—with counter-power expressed as the counter-inauguration. The counter-inauguration was an assemblage of older and newer media technologies, genres, norms, behaviors, and organizational forms; an emergent but decisively integrated confluence of politicians, professional political staff, journalists, citizen activists assembled as crowds, NGOs, online political communities, mobile devices, social media platforms, websites, television shows, and sporadically engaged members of the public. Crucially, the assemblage also included the highly connected global networks of people who participated in the Women’s Marches in more than eighty countries across the world. These geographically distant others were brought into close temporal proximity to the events in Washington by the affordances and uses of mobile digital media hardware and software. This enabled them to become simultaneously a force, acting in real time, on the events in Washington, as well as on how those events were being mediated across the United States and the world. The highly global character of this assemblage, made manifest by the distant crowds’ self-mediation via social media, was important in generating real-time solidarity, enthusiasm, (p.249) and momentum for those engaged in the marches in Washington and across the United States. Through their acts of self-mediation and the remediation of these acts by journalists, these distant crowds were, in effect, pulled in to the center of the location that mattered—the National Mall. Here, they became important ancillary evidence of the extent of the opposition to Trump, and, in turn, they reinforced and enhanced the capacity and momentum of those in Washington eager to embarrass the president with a show of strength for the cause of women’s rights.

In a similar virtuous circle of mediation, the size of the Women’s Marches also spurred on journalists, whose news values were always likely to require that they show their audiences the sheer size of the crowds. After all, it was the resources of professional journalism—Lucas Jackson’s photo from the Washington Monument and Reuters’ news article comparing the 2009 and 2017 crowds—that contributed to the very possibility the inauguration might be interpreted as a failure for Trump. The marchers capitalized on this framing, but literally democratized and distributed its agency, by becoming an embodied, physical manifestation: a show of strength greater than Trump’s supporters could assemble. All the while, the marchers acted in the knowledge that their physicality would, in turn, be mediated by those same journalists—the ones who were looking to mobilize and represent the Marches’ actions in ways that demonstrated the same flawed Trump logic they were seeking to expose in their fact-checking articles about the inauguration. These actors cooperated and competed to develop webs of meaning that would create the conditions for exercising power. These were the resources that enabled the power to define what the inauguration would come to symbolize.

Spicer did not solve his problem.

These two stories from the counter-inauguration come from what should have been the apotheosis of Trump’s presidential campaign. This should have been a period of discipline and control. Instead, it was characterized by disorganization, contestation, and chaos. It signaled the new administration’s potential fragility in the face of the systemic interdependence between elite political and bureaucratic resources, journalistic counter-framing, and digitally enabled, activist counter-power—the forces that, as we have seen, now shape so much of how politics unfolds.

This was a fitting end to an election that will rightly engage an entire generation of scholars eager to make sense of the role of media in Trump’s rise from rank outsider to Republican primary nominee and eventual victor. In the remainder of this chapter I analyze what I consider to be the key episodes of the 2016 U.S. presidential campaign. I show how the hybrid media system approach can make sense of the campaign and assess the extent to which 2016 marked not only an intensification but also a rebalancing of the hybrid media campaigning that proved so decisive in the rise of Barack Obama.

(p.250) Premediating Trump: From The Art of the Deal to The Apprentice

The historical context for understanding the mediation of Trump during the 2016 campaign is his autobiographical memoir and business self-help manual, The Art of the Deal. Published in 1987 and ghostwritten by journalist Tony Schwartz, the book was a million-copy bestseller that effectively launched Trump’s career as a celebrity capitalist. In the late 1980s, just as the neoliberal, deregulatory shift in Western economic policy was loosening political control over the banks and the financial system, the proliferation of cable and satellite television was also fragmenting public communication, offering new opportunities for those with the right resources to construct and project a persona based on aspirational lifestyle norms and the entrepreneurial “American dream.” Trump benefited from, and contributed much toward, the success and popularization of both of these historical shifts.

The Art of the Deal was important for its construction of Trump as a pugilistic, entertaining risk-taker and, perhaps most crucially, a “winner” whose approach to life and business demonstrably got results. Yet, as Todd Gitlin has shown, the book also punctuated an earlier period, during which Trump, to promote his persona, had interacted on a regular basis with journalists and editors at the major U.S. tabloids, particularly the New York Post and the Daily News (Gitlin, 2017).

The Art of the Deal paved the way for a truckload of similar books to roll through the 1990s and 2000s, culminating in a book that had a major influence on the themes of Trump’s run for office: 2011’s Time to Get Tough: Making America #1 Again. Although it did not attract much attention from news organizations at the time, this book’s many policy recommendations—from taxing China “to save American jobs” to restricting immigration and repealing the Obama administration’s healthcare reforms—were the foundation of many of Trump’s 2016 rally speeches.

Trump’s hybrid autobiography–self-help book franchise is further evidence of the importance of what Richard Grusin has termed “premediation”: “the cultural desire to make sure that the future has already been pre-mediated before it turns into the present (or the past)” (Grusin, 2010: 4). As I argued in chapter 7, Obama’s historic success in the 2008 campaign was premediated by his bestselling autobiographies Dreams from My Father (1995) and The Audacity of Hope (2006). But Trump took this heroic narrative approach to new heights—or depths, depending on your taste. By 2016, he had meticulously packaged up his life story and business lessons into bite-sized chunks of wisdom, complete with online to-do lists on his website for the time-pressed, aspiring entrepreneur.

Thus, as the 2015 primary season got underway, Trump’s books were ready to be mined by journalists looking for a steady stream of anecdotes about the candidate’s behavior and attitudes. Long before his political ambitions became clear, (p.251) Trump’s persona had been sensational enough to warrant front-page coverage. But once he announced his candidacy, the floodgates opened. During the course of the campaign, dozens of high-profile news articles emerged that combined fragments from the book franchise, quotes from magazine interviews, tidbits from celebrity gossip columns, and quotes from Trump’s campaign statements (for a good example of the genre, see Mayer, 2016). The fact that so many of Trump’s views and habits were already in the public domain before he decided to run for office proved irresistible to journalists eager for a hook—a salacious tale of vulgar excess here, a moment of psychological transgression there—that would spice up their campaign coverage and reach more readers in a media system that had become even more competitive since 2008.

As Obama did in 2008, Trump himself built directly upon the mediated context he had already helped shape with his books. On June 14, 2015, standing in the lobby of Trump Tower on New York City’s Fifth Avenue to announce his candidacy, Trump said this:

So I’ve watched the politicians. I’ve dealt with them all my life. If you can’t make a good deal with a politician, then there’s something wrong with you. You’re certainly not very good. And that’s what we have representing us. They will never make America great again. They don’t even have a chance. . . . Our country needs a truly great leader, and we need a truly great leader now. We need a leader that wrote The Art of the Deal. (Washington Post Staff, 2015)

If Trump’s book franchise laid the foundations for his rise to political prominence, it did so, not as a set of isolated artifacts, but as part of a broader assemblage of broadcast media and the web. The most important vehicle here was the NBC television reality game show The Apprentice. Trump was the star of its first fourteen seasons from 2004 to 2015. The show’s format, with its weekly rituals of desperation, interrogation, and rejection, was contrived to position Trump as the ultimate arbiter of what constitutes success in the modern workplace, and society in general.

Looking back at the mediation of the 2016 campaign, it is clear that The Apprentice was a decisive cultural and political force in the construction of Trump’s authority. This went far beyond the shtick of his infamous catch phrase, “You’re fired!” In the twenty-first century, the lines between work and private life have become increasingly blurred as work has come to play an important structuring role in everyday life. The workplace has become probably the nearest thing there is to an almost universally resonant public space in which we learn how leadership, authority, and power can decisively shape our fates. It should have come as no surprise, then, that Trump’s show would become a huge hit with audiences, nor that it would be important for the development of the “Trump-as-winner” brand. The norms and practices of the (p.252) fictional workplace of The Apprentice served as condensed, exaggerated, and distorted versions of the norms and practices that animate workplace relations in the real world beyond “reality” TV. But, like most niche reality formats, ultimately the show worked because it held up a mirror to society at large (Franko, 2006: 250). It is thus important to consider the political work that The Apprentice did for premediating Trump’s run for the White House.

In one sense, this is straightforward enough. In the words of Elizabeth Franko, The Apprentice was a “highly centralized dictatorship” (2006: 249). Trump was at its apex, dispensing advice to viewers straight down the barrel of the camera lens, meting out justice to desperately competitive participants who knew they must cooperate if they were to have any hope of surviving their manufactured (and often genuine) precariousness. In this world, the self-aggrandizing Trump always began as the winner and always ended up as the winner. Even when, at the end of a season, the final apprentice had been chosen, Trump still won, because the prize was a position running one of his companies. His status was unassailable and was presented as such due to his background and experience earned externally to the show, in the “real” world of business.

All of this televisual work was reinforced by the show’s companion website. Sponsored by the American Management Association, it featured weekly hints and tips on how to improve one’s leadership skills. The spoils of Trump’s success were frequently on display. The conspicuous consumption and status symbols—the helicopter, the New York penthouse, the hotels, and the goods that participants were asked to sell, such as Trump-branded bottled water—all contributed to the profoundly inegalitarian symbolic structure. But the internal rules of the game were contrived to produce and reproduce this hierarchy.

The second, less obvious, sense in which The Apprentice mattered for Trump’s campaign derives from one of the show’s most often-repeated tag lines: “It’s Not Personal, It’s Just Business.” The meaning of this phrase bears some scrutiny. It rests on a separation between ethics in the world of business and those that apply in our “personal” lives. In business, anything goes; any strategy to defeat your opponents is fair game, because, in another of the show’s catchphrases, “Winning Is Everything.” In personal life, different, more collaborative, solidaristic, and civil norms may apply, but these will only cloud judgment in the world of business, where competition, individualism, and incivility get results. This—the central moral narrative of a show that ran for more than a decade before Trump announced his candidacy—was a theme that recurred time and again throughout the primaries and the general election campaign. Trump traveled from rally to rally, televised debate to televised debate, personally insulting other candidates, journalists, celebrities, and protestors; settling scores and stoking professional media coverage with an extraordinary litany of “politically incorrect” exaggerations, half-truths, distortions, and lies about others and his own record.2

(p.253) The Apprentice’s moral code of “It’s Not Personal, It’s Just Business” became the force behind much of Trump’s behavior during the campaign. It spurred his continuous violations of so many of the established civic norms of election campaigning. This presented professional journalists with an acute dilemma. How should they report on a candidate who was willing to trample on so many established expectations about how politicians were supposed to behave?

Seriously, Literally, or Both? Exploiting the Dilemmas of Journalistic Hybridity

The driving moral code of Trump’s vituperative campaigning style, “It’s Not Personal, It’s Just Business,” is key to understanding Trump’s exploitation of the dilemmas of contemporary journalism.

On September 23, 2016, with election day looming, the Atlantic magazine published a short article entitled “Taking Trump Seriously, Not Literally.” Part promotion for Trump, part reflection on the role of fact-checking in journalism, the piece was written by Salena Zito, a reporter and former Republican presidential campaign worker. Zito argued that journalists had failed to understand why Trump had won the Republican nomination and was now a contender for the presidency. When Trump made racist, sexist, or xenophobic remarks, exaggerated claims or massaged statistical evidence, elite media had treated him either as an amateur to be ridiculed, or had responded with manufactured outrage that further fueled Trump’s publicity-hungry campaign and its theme that elite media were biased. This was a disturbing analysis that sent shock waves through professional media organizations. The “seriously, not literally” analytical couplet became a major theme of the closing weeks of the campaign.

Trump’s strategy was to prime attention to issues that he thought might grow his electoral base and resonate with the social groups that post-election analyses showed were essential to his success: older, white, working-class voters in the small towns and cities of the Rust Belt in Michigan, Wisconsin, Iowa, and western Pennsylvania (Pacewicz, 2016). If this meant speeches based on racial slurs against Mexican immigrants, mysogynistic attacks on women journalists, a constant rejection of “political correctness,” and going beyond the realm of “the facts,” then he saw that as a price worth paying.3 The response from elite journalism was to fact-check and highlight Trump’s illiberalism with headlines full of outrage—in other words, exactly the kind of coverage that a right-wing, insurgent candidate requires if he is to build a viable electoral coalition by reaching out to supporters not traditionally aligned with the Republicans. And this was exactly the kind of coverage that would galvanize that support by constantly showing elite media for what he believed they were: “coastal liberals” who did not understand “middle America.” Central to this (p.254) goal was a media strategy that ruthlessly, and perfectly rationally, exploited the systemic interdependence between older and newer media.

Trumping Professional Media

In my analysis of the 2008 U.S. election (chapter 7), I showed that a central force in shaping the contemporary campaign is the real space-internet-television nexus. Contrary to predictions that digital media would come to displace television as the most important campaign medium, instead there has been a growing systemic interdependence between television and digital media. This context encompasses the now-classic campaign media events, such as live televised candidate debates, scheduled set-piece interviews, and press conferences. Yet layered into this context of mediation, interacting with it at all times, is physical spectacle.

Physical spectacle continues to matter a great deal for how campaigns are mediated. Campaigns now rely on a combination of digital media, television, and particular formations of physical space: the inescapably material, embodied, and geographically proximate experiences of rallies, marches, town hall meetings, and staged campaign gatherings. Often theatrical, these campaign events remain essential to projecting a candidate to the public and to journalists. They are a means of attracting broadcast and print coverage because journalists crave symbols that visibly convey the political drama as it unfolds, such as displays of enthusiasm, candidate and activist authenticity, and the momentum that builds around the performance of heroic individuals (Alexander, 2010; Mast, 2016). Rallies and marches provide partisans and activists with resources, such as status updates, photos, and video clips, that they can distribute in their interpersonal networks. For campaign managers, events also provide opportunities to use digital media, particularly email and social media platforms, to gather information that may be used to mobilize supporters during the remainder of a campaign.

The Trump campaign exploited, but also partly reconfigured, the real space-internet-television nexus. Central to this process was Trump’s staggering level of social media visibility. In this he far outpaced Hillary Clinton, not only in numbers of followers, but also in the social media engagement metrics that reveal the extent to which the American public now uses social media to follow politics. By the close of the campaign, Trump had 12 million followers of his Facebook account, 12.9 million Twitter followers, and 2.92 million Instagram followers. Clinton bowed out with 7.9 million Facebook followers, 10.2 million Twitter followers, and 2.93 million Instagram followers (Meyer, 2016). Trump clearly had the edge, but these basic numbers do not tell the whole story. For that we need to consider engagement metrics: comments, shares, likes, Facebook “reactions,” and retweets. We need to consider how these made a difference and why there was such a gap between Clinton and Trump.

(p.255) Metrics of Engagement

In the late 2000s, social media companies began to generate revenue by opening up their platforms to advertisers. They soon realized that they needed reasonably clear and intuitive ways of measuring how individuals interacted with the content they found in their news feeds and streams. Just as important, social media companies needed to encourage users to spend more time on their sites, so they could demonstrate to advertisers that users were devoting attention to advertisements. This partly accounts for the introduction of click monitoring, likes, shares, and retweets.

Metrics such as these are basic, and I am not arguing that they can be used to make inferences about public opinion. But they have clearly become reasonably stable artifacts and products for which brands, celebrities, and candidates will now pay. These data allow us to draw some comparisons between candidates and augment the complex mix of variables that determine the success or failure of a campaign strategy.

With this in mind, how did Trump’s and Clinton’s social media engagement levels compare? What emerges is a stark picture of Trump’s engagement advantage. From January 1, 2016, to November 6, 2016, on Facebook, Trump’s posts racked up a total of 208.1 million likes, comments, and shares (Meyer, 2016). In contrast, Clinton’s gathered only 72 million. During the same period on Instagram, Trump’s posts received 53 million likes and comments, while Clinton’s received 31 million. On Twitter, the platform to which journalists flock for sourcing their stories, the engagement gap was larger still: Trump’s 89.5 million likes and retweets dwarfed Clinton’s 41.6 million.

Of course, these simple metrics tell us nothing about the content of what was shared or how different types of content were related to levels of engagement. At the time of this writing, we do not know the proportion of Trump’s engagement metrics that came from liberals circulating his posts in order to share their outrage at his opinions, for example. But most salient here is how the social media engagement gap enables a candidate to exert greater agency in the broader media system than his or her opponents. What kinds of media-systemic advantages now accrue to those who can open up such a decisive lead? Answering this question requires sensitivity to the context of the 2016 race and attention to three essential forces: recognition, credibility, and momentum.

Recognition, Credibility, and Momentum: Social Media Drive Traditional Media Coverage

Trump was an insurgent candidate and was not part of the Republican establishment. He had celebrity capital but very little political capital. To translate his celebrity capital into political capital and go on to build support, he required coverage—of any (p.256) valence—by professional media, particularly in the early stages of the Republican primaries, but also throughout the entire campaign. He had to demonstrate to editorial gatekeepers and political reporters at the nation’s most respected media organizations that he was a contender for the Republican nomination and that he could rival Clinton in the general election.

For journalists eager to report on the drama of a campaign, social media metrics matter (Anderson, 2013). Due to the large numbers of Americans that use social media platforms on a daily basis, metrics are often used as proxies for the levels of interest that a candidate is generating among the public. Metrics now have a place in newsrooms, alongside opinion polls and forecasting. In 2016, social media logic thus jelled perfectly with older mass media organizational logic. The result was always likely to be greater publicity for an insurgent, as journalists went hunting for high-impact tweets that they could embed in their news articles—with the numbers of retweets, likes, and replies all prominently displayed.

But what about the thorny issue of content, particularly the proportion of the social media engagement that was critical of Trump? “There is no such thing as bad publicity” is such an awful cliché that it almost gives me physical pain to write the words. But again, context is everything here. In a media system in which countless acts of engagement occur every second, there are some fields of activity where the specific intent or valence of an act of engagement might not matter as much as how those acts combine to produce sheer mass and ubiquity. Trump’s challenge was to get a foothold in the race and to continue to convert his celebrity capital into political capital by attracting further “serious” political coverage. Social media were crucial in overcoming this challenge.

Trump’s campaign was therefore an intensified and distorted version of the model that had been used by Barack Obama in 2008 and 2012. During the early stages of the race (but not the later stages, as I shall discuss in the following), what Trump lacked in targeted advertising and data-driven campaigning at the precinct level he made up for in social media engagement that caught journalists’ attention. Obama’s campaigns went far beyond the mere generation of metrics-as-proxies; they empowered large numbers of activists to become involved in direct interactions with the electorate. For Trump, as for Obama, online supporters formed an essential component of the campaign assemblage. But Trump prioritized a “purer” social media strategy. He personally tweeted and updated his Facebook page with fresh content that he knew would be circulated by supporters and opponents alike. Aside from the recognition benefits that would accrue from the horizontal sharing of his opinions in the interpersonal networks of his followers, this approach enabled Trump to demonstrate his impact to journalists. Journalists, in turn, provided him with the credibility and recognition he needed to reach the broader public, including all-important independent and switch voters in swing states. The regularity and timeliness with which Trump intervened on social media contributed greatly to the momentum that is such an (p.257) important long-term resource in the brutally drawn-out American campaign. It is also important to bear in mind that Trump’s Twitter following had been built up during his days on The Apprentice, and it is clear that he had learned a great deal about what works in the celebrity-infested waters of this platform. Trump’s aggressive tweeting patterns had become well-known to his followers, and they were an essential part of his television persona.

During the campaign, Trump tweeted at times of the day that were deliberately calculated to have the greatest possible influence on newsroom editorial meetings across the country. He was also more likely than Clinton to post links to news articles (Enli, 2017). In a world where most politicians have decided to control their online personas and devolve social media work to their staff, it was obvious that Trump posted many (though not all) of his own tweets. The fact that these messages often included spelling errors, uppercase SHOUTING, and exclamation points (BAD! Sad!) only added to the construction of Trump as an authentic, “ordinary” guy. As Gunn Enli’s (2017) analysis of Trump’s and Clinton’s tweets has shown, this “amateur” rhetorical style was present in 54.5 percent of Trump’s tweets but only 12.9 percent of Clinton’s. This development reflects a broader, and growing, affinity among elite political actors for the conversational and informal discourse that is prevalent in social media environments. It is a further installment in the growth of a new style of campaigning that now coexists alongside the highly controlled discursive style of the “professionalized” model that was dominant at the height of the broadcast era (Chadwick, 2006: 144–176). Trump’s social media campaign rudely and unexpectedly spoke to several of the pathologies of scholarly research on the internet and campaigns. Scholars have long asked: Why are politicians so inauthentic and robotic online? Why are they so controlled, remote, and unengaging? Why do they not say what they really think and engage with voters? (Chadwick, 2006: 144–176; Vaccari, 2013).

That said, we should not mistake Trump’s amateur social media language for interactive deliberation. There is very little evidence that Trump, or Clinton for that matter, engaged in direct conversations with their Twitter and Facebook followers. Trump was much more likely to retweet members of the public, so the argument might be made that he was indirectly engaging with his supporters. But an analysis from the Pew Research Center reveals that these types of retweets took two main forms: Trump retweeted congratulatory messages from well-wishers and messages that were critical of his opponents. A good example was Trump’s retweeting of tweets from ordinary members of the public that referred to the Fox News anchor, Megyn Kelly, as a “bimbo” (Pew Research Center, 2016b: 21). We also know that Trump did not post all of his own tweets: often he left that job to his digital campaign manager, Brad Parscale, who had a database of 400 “template tweets” that he used to fire off tweet storms on a minute-by-minute basis to coincide with Trump’s media appearances (Green & Issenberg, 2016). While deprofessionalization may have been in evidence, so too was a reprofessionalization, of sorts.

(p.258) Trump may have used social media to present himself as an amateur, but he also used it to portray himself as a showbiz celebrity. By 2016, journalists had become used to staid and restrained tweets from politicians. In constant amazement at Trump’s gall, they lapped it up. Trump’s targets were often from the world of entertainment in which he moved, such as Meryl Streep, whom he described as being “overrated” following her denouncement of him at the Golden Globes ceremony, at which she received a lifetime achievement award (Izadi & Wang, 2017). Trump’s authenticity therefore rested upon a double movement: he was at once the ordinary guy and the billionaire who moved among, and interacted with, entertainment celebrities. Both of these characteristics, fused in his Twitter stream, helped with his goal of being presented, not as a career politician, but as an outsider hell-bent on going to Washington to “drain the swamp” of its wasteful bureaucracy, just as Republican and former Hollywood actor Ronald Reagan had promised in 1980.

Trump also tweeted about what he was watching on live television, particularly if it was the conservative channel Fox News. These were not random acts. They were messages designed to create solidarity with the conservative activists and reporters who he assumed would be watching along with him or who, later in the day, might be using social media to discuss the issues raised on a show. A good example is a Trump tweet that exclaimed that those who burn the American flag should be jailed or have their citizenship rescinded. Moments before the message, Fox News’ Fox and Friends had carried a story about the burning of a flag on the campus of Hampshire College in Massachusetts (Hess, 2017). Trump was the first dual-screening candidate. In itself, this is a reflection of how seriously he took Twitter as a medium for setting the mainstream news agenda.

Trump’s transgressive tweeting thus helped him in several decisive ways, though very few of these derived from his use of Twitter in isolation. Twitter enabled him to establish himself early on in the Republican primaries as one of the front runners in an unusually large field of seventeen candidates. It has become a commonplace of U.S. election studies that in order to gain “earned” (i.e., journalistic, not bought) media coverage during the early stages of the race, a candidate requires two things: favorable poll ratings and a demonstrable ability to raise money (Lawrence & Boydstun, 2016). But in Trump’s case, because his established celebrity television persona had already created a substantial Twitter following, his candidacy was already news. As soon as he announced his run in June 2015, he started to receive neutral or favorable coverage from mainstream news outlets, in plentiful quantities (Patterson, 2016). This radically reduced his campaign’s need for paid advertising. Across eight major news outlets (CBS, Fox, the Los Angeles Times, NBC, the New York Times, USA Today, the Wall Street Journal, and the Washington Post) for the pre-primary period January 1–December 31, 2015, Trump received an estimated $55 million of ad-equivalent neutral or favorable coverage. The New York Times provided $16 million worth of this equivalent (p.259) coverage, an amount that exceeded Trump’s entire ad spending during the pre-primary period. And this earned media came despite Trump’s poor showing in the early opinion polls and his neglect of campaign fundraising during the primaries (Patterson, 2016).

Once the primaries were underway, Trump’s earned media advantage over his Republican rivals widened still further. During the all-important month of February 2016 —the buildup to Super Tuesday, when thirteen states held their primaries—Trump spent just $10 million on advertising. This contrasted with the $82 million spent by Jeb Bush and the $55 million spent by Marco Rubio in that month. Even Democratic outsider candidate Bernie Sanders spent almost three times as much on advertising as Trump during this month (Confessore & Yourish, 2016). According to data gathered by the New York Times from media analytics platform mediaQuant, which analyzes online news, broadcast news, print news, blogs, forums, and Twitter, by March 2016 Trump had secured an estimated $1.9 billion of ad-equivalent earned coverage—six times more than Ted Cruz, almost ten times more than Jeb Bush and Marco Rubio, and, tellingly, more than double Hillary Clinton’s total. MediaQuant is a business and does not release the precise details of the sources for its index. We know, for example, that it includes Reddit and Twitter advertising equivalence metrics among its social media sources but not equivalence metrics for Facebook (Bialik, 2016). Still, what matters here is not so much the absolute accuracy of the index, but its usefulness for revealing the differences between the 2016 candidates. By the November election day, Trump had earned $4.96 billion of ad-equivalent coverage, while Clinton trailed at $3.24 billion. Trump outperformed Clinton across all media types, with particularly large margins for online news sources, blogs and forums, and an extraordinary advantage of 142 percent for Twitter. Overall, Trump earned more than three and a half times more coverage than Obama had earned during the 2012 race. This is all the more significant given that a sitting president, as Obama was in 2012, can always rely on large volumes of earned coverage (Harris, 2016).

Driving Coverage to the Nomination

What explains the elite media coverage that, in turn, enabled Trump to gain momentum and move to the front of the crowded Republican primary so quickly? Based on a content analysis of mainstream sources during the pre-primary coverage, which showed the extent of Trump’s free media coverage, political communication scholar Thomas Patterson argued that traditional news values explained this puzzle. Journalists, he suggests, were “behaving in their normal way,” by conveying to their audiences a novel and at times sensational Trump insurgency. Trump, Patterson says, “exploited their lust for riveting stories” (Patterson, 2016).

But is this account complete? Patterson’s analysis excluded social media entirely. Yet it was Twitter that forged the link between Trump’s prior celebrity capital and (p.260) journalists’ fascination with his political ambitions. Trump saw Twitter as a means of intervening in the political information cycles of the campaign to boost his earned media coverage. Also important to his approach was directly inspiring—and, in part, feeding off—a growing army of conservative online activists, who gravitated to right-wing online news sites such as Breitbart. Founded in 2007, by 2016 Breitbart had made it into the top fifty most popular websites in the United States. It had more than 2.3 million Facebook followers, and attracted 18 million homepage visitors a month during the campaign (Kreiss, 2017). A post-election automated keyword analysis by Yochai Benkler and colleagues of over 1.25 million news stories revealed that there were surprising similarities between the most prominent themes (immigration, Clinton’s character, jobs) of both Trump’s speeches and Breitbart’s news articles and the most prominent themes professional media used to report on the campaign (Benkler, et al., 2017). Disentangling whether it was Trump’s direct influence, or the right-wing news network’s indirect influence, that shaped professional media coverage is impossible with Benkler and colleagues’ data. But the key point here is that both are likely to have played a role. Trump’s inflammatory tweets, rallies, and press conferences were always likely to be news. Yet social media sharing by Breitbart readers, made manifest in the form of visible metrics of engagement on Facebook and Twitter, further incentivized journalists to report on the controversy and the enthusiasm generated by Trump’s tweets, rallies, and press conferences. It was all news.

A further point here is a contextual one about the changing structure of attention online. Since the latter part of the first decade of the 2000s, social media have decisively reshaped the incentive structures for online news. Social sharing optimization (SSO) has come to replace search engine optimization (SEO) (Karpf, 2016a: 93–122). This, in turn, is changing how attention to politically useful information is distributed around the media systems of the advanced democracies. The earliest model for driving attention was banner advertising. This was followed by a rush to use Google’s AdWords platform to secure placement against user searches. Soon after Google’s ascendancy in the advertising market, new digital news providers emerged, like Demand Media, which owned so-called content farms (like eHow and Cracked), designed to attract Google search users. Demand appeared to have mastered this SEO approach, but then social media platforms came along with a different model. As users flocked to Facebook and Twitter, it soon became clear that many individuals saw these as destinations not only for consuming news content, but also for sharing news in their interpersonal online social networks. By 2015, 63 percent of Americans with a Facebook or Twitter account said that they turned to these sites for news. Given that, by 2015, 62 percent of the U.S. adult population used Facebook and 20 percent used Twitter (Duggan, 2015), these were important shifts.

SEO staggered on for some time as the organizing logic of online news, but by the mid-2010s it had been eclipsed by the logic of social sharing. New online news organizations like Buzzfeed and Vox were specifically designed to have social (p.261) sharing “baked in,” not least because they announced their news on Facebook and Twitter at the same time as on their websites. Attention to news is now much less reliant on Google and much more reliant on the machine learning algorithms that distribute attention on social media platforms. And, while we cannot know the full details of these algorithms because they are commercial secrets, what we do know is that timing and engagement matter. Posts that receive high levels of user engagement in the form of shares, likes, comments, and retweets over a short period of time are more likely to show up in users’ news feeds (Bucher, 2012, 2018). This changed context, with its new audience expectations, was ripe for a candidate like Trump, who wanted to master the now-integrated temporal rhythms of professional news media and social media sharing.

With this contextual shift in mind, what can we say about the success or otherwise of Trump’s social media strategy? Evidence comes from Chris Wells and colleagues’ study of the factors that drove news media attention to Trump during the primaries (Wells, et al., 2016a; see also Wells, et al., 2016b). Wells and his team built a large-scale, longitudinal data set covering an unusually wide range of sources that they gathered during the period from Trump’s announcement in June 2015 to the date of his primary victory on May 4, 2016. They wanted to see if there were any identifiable causal relationships between Trump’s Twitter activity, campaign events such as debates, interviews, and rallies, the growth over time of his Republican delegate count (as he steadily notched up primary victories), and the amount of mainstream media coverage he received.

To enable this research design to work, Wells and his team needed the right data and the right method—not an easy task given the complexity of a presidential campaign. For each day during the eleven-month primary campaign, they tracked Trump’s delegate count, which is published by the Republican Party shortly after each primary or caucus. They gathered a one percent sample of all retweets of Trump’s tweets from Twitter and they logged the timing of the twelve official Republican primary debates. From Trumpshow.info, a website that aggregated all of Trump’s rally and media appearances during the campaign, they added data on events of three kinds: staged public events, such as campaign rallies; planned media events, such as interviews and press conferences; and what they termed “unscheduled media appearances,” which captured Trump’s unusual habit of personally calling in, uninvited, in the middle of television and radio talk shows.4 Using the Nexis news database (a global archive of all newspaper content), Wells’s team counted how many news stories mentioning Trump appeared daily in the New York Times, the Washington Post, USA Today, and on the Associated Press newswire service. Finally, to capture the huge quantity of material that newspapers now publish solely online, they also counted mentions in blogs managed by the New York Times and the Washington Post.

While not a perfect data set—it contained no material from Facebook or local newspapers, nor from the popular news and commentary site Reddit, which was (p.262) unexpectedly important during the 2016 campaign (Chadwick & Stromer-Galley, 2016)—Wells and his team went a long way toward capturing the data that matter for exploring interactions between campaign spectacle, social media, and broadcast and newspaper coverage. They had time-series data, which meant they could use two particularly appropriate statistical methods—Granger causality and autoregression. These allow researchers to apply statistical controls to identify the temporal direction of causality; in other words, they enable us to see whether Event A at Time 1 actually played a statistically significant role in shaping Event B at Time 2. The big question here was: Did Trump’s tweets lead to greater news coverage, or was it other factors, such as his staged media appearances?

The findings were clear. Increases in the volume of retweets of Trump’s tweets led to increases in news articles and blog posts. Things did not work the other way around. In other words, there was no evidence to suggest that increases in retweets of Trump’s tweets were caused by increases in the number of news articles and blog posts being published in the press. The findings also showed that Trump was more likely to post tweets during periods when his news and blog coverage was relatively quiet, confirming the theory that he used Twitter to stoke the fires of coverage.

Yet, in a confirmation of the real space-internet-television nexus, Trump also benefited from the traditional staged media events of the campaign, such as press conferences, rally coverage, and set-piece interviews. The data show that professional media quickly learned to cover Trump’s press conferences and to invite him to interviews, and these events would be more likely to be covered in subsequent news stories (though not in news organization blogs). Trump’s habit of calling in unannounced to radio and television talk shows also had an influence on the amount of news articles and blog posts, but only before the primary elections began, although arguably this was the period when such interventions were more crucial for establishing momentum. The televised primary debates were also important for driving increases in both news articles and news organization blogs. This is testimony to the disturbing power of Trump’s often outrageous remarks during these events. The final nail in the coffin of the argument that the press were simply doing their usual job by recording Trump’s increasing electoral popularity is that there was no statistical relationship between his delegate count and the amount of coverage he received (Wells, et al., 2016a). In other words, professional media did not seem particularly interested in framing their reports of Trump in terms of his growing support.

Trump’s insurgent hybrid media strategy worked. It was a combination of his Twitter use, the volume of retweets by his Twitter followers, and staged media events such as press conferences, interviews, and candidate debates, as well as his direct interventions in broadcast news shows, that enabled him to gain the publicity he required from elite media organizations. This was not a process of disintermediation. Contrary to much of the popular commentary during the campaign, Trump (p.263) did not use social media to “bypass” professional media; he used social media to influence professional media. This was not disintermediation; it was intermediation.

Trump’s Data Campaign and the Switch to Facebook Advertising

Focusing solely on Trump’s use of television and social media might give the impression that he did not develop a data-driven ground campaign. This would be misleading, though the reasons why this is the case require explanation.

Since 2004, the meaning of the term “ground campaign” has been redefined by a new generation of campaign personnel recruited to develop data-intensive strategies. This has involved the integration of broadcast-era “war room” professionalism, online fundraising, data science, field organizing, and the mobilization of large armies of volunteer campaign workers operating at the precinct level. Following the 2004 election, this combination of data, analytics, and the ground war evolved into the campaign norm for both the Democrats and the Republicans, albeit with different levels of intensity given the Democrats’ comparative advantage in 2008 and 2012 (see chapter 6 herein; Hersh, 2015; Kreiss, 2012, 2016; Nielsen, 2012).

Despite the growth of online campaigning, broadcast-era logics of top-down presentational professionalism and tight control of campaign messaging integrate surprisingly well with this new approach (see chapter 6). For the Democrats in particular, discipline and calibration were central to the turn toward data and analytics. This involved campaign elites’ increasing use of experimental data science methods to interrogate large-scale aggregations of behavioral information from public voter records, off-the-shelf marketing databases, and digital media environments, with the aim of organizing and mobilizing key segments of the electorate to vote and to publicly and privately share their voting preference with others (Chadwick & Stromer-Galley, 2016: 284–285). Meanwhile, the Republicans’ 2012 defeat also jolted the GOP to increase investment in their digital infrastructure to keep pace with the Democrats (Kreiss, 2016: 168–203; Vogel & Samuelsohn, 2016). The analytics turn has produced new and surprising sources of organizational power inside both major parties. Digital media elites have embedded their expertise and operating norms, and these differ from those prevalent among staff who worked to perfect campaign media strategies during the broadcast era. How did the 2016 Trump campaign measure up against this context?

Fundraising was certainly important to Trump. Despite the fact that he announced on several occasions during the campaign that he was planning to fund his own race, once the primaries were over, he sought other sources of income. According to Federal Election Commission filings, by election day Trump had raised a total of $957.6 million. This came from his personal campaign, party and joint fundraising (p.264) committees, and political action committees (PACs). All but $65.8 million of this money came from sources beyond Trump (Washington Post Staff, 2017).

Clearly, then, Trump did not neglect fundraising in any absolute sense. He did, however, neglect it in a comparative sense. Judged against Hillary Clinton’s in 2016 (and, indeed, both Barack Obama’s and Republican Mitt Romney’s in 2012), Trump’s fundraising totals were timid. Clinton raised $1.4 billion from all sources in 2016. Her personal campaign total of $623 million was nearly double Trump’s $335 million. In 2012, Obama raised a staggering $731 million from his personal campaign, but even the lackluster Romney managed to raise $474 million in that race (Washington Post Staff, 2017). Trump came nowhere near these totals, reversing the post-1990s trend toward ever-increasing spending in U.S. presidential elections. This might lead us to the conclusion that one of the chief goals of a data-driven campaign—acquiring large numbers of donations—was not a priority. But if we consider the type of donations Trump received, the precise nature of the Republicans’ approach to data starts to unfold.

It is now well established that if a campaign prioritizes online fundraising it can pursue, and is likely to receive, large quantities of small donations. This is because the marginal costs of raising money online are low when compared with other methods, such as receiving checks through the mail (Anstead, 2008; Bimber, 2003). A campaign can also use its email list to reach beyond the highly engaged activists who are more likely to make larger donations. Since 2004, the Democrats have consistently outperformed the Republicans on small-dollar fundraising. In chapter 6, I showed in some detail how this worked. Small-dollar online fundraising is based on three basic ingredients: an accurate email list generated from sign-ups at rallies and online after media events, particularly televised debates; careful use of email to target likely donors; and a donation “subscription” model that encourages repeat giving of modest amounts. Success in small-dollar fundraising is therefore a rough but reasonably good measure of whether a data-driven campaign is working.

In 2012, 32 percent of Obama’s money was from donations of $200 or less, dwarfing Mitt Romney’s 5 percent. In 2016, Clinton could not match Obama’s record; only 16 percent of her money came from small donations. But the most impressive story on fundraising in 2016 was how Trump turned around the Republican Party’s poor record on small donations. Trump received 26 percent of his funds from small donors, more than five times the percentage Romney received in 2012. Trump’s small-dollar haul of more than $100 million is the most any Republican candidate has received in sub-$200 donations (Washington Post Staff, 2017). This is all the more impressive given that Trump did not even begin his email fundraising drive until June 2016, after the primary and well into the presidential campaign (Goldmacher, 2016). Trump also received far less money from PACs than Clinton. While we need to bear in mind that Trump also received huge amounts of money from large donations, it is clear that he also managed to reach beyond the usual suspects that had fueled the fires of Republican campaigns (p.265) before 2016. Clearly, Trump’s digital campaign worked in this regard. But what shape did it take more generally?

The Evolution of Trump’s Digital Strategy

The world of digital campaigning is notorious for its toxic blend of hype and secrecy, not least because the firms that sell their services to candidates often try to protect their commercial reputations. This is especially true of the closing stages of a campaign, when firms set out their stalls to attract business at the next election cycle. We need to bear this in mind when trying to establish what we know about Trump’s digital strategy (Karpf, 2016b). It is also worth noting that, throughout the campaign, the vast majority of opinion polls showed that Trump was going to lose (RealClearPolitics, 2016). There was little in-depth reporting from inside Trump’s war room because journalists have had a bias toward success stories in which digital methods help winners. Trump also kept tight control over access to his digital campaign team (Green & Issenberg, 2016). It is easy to see how the narrative caught hold that Trump’s campaign lacked sophistication and was stuck in a pre-digital time warp (Marshall, 2016; Sifry, 2016).

A few weeks after the election, an extended article about Trump’s supposed use of psychometric targeting appeared on the news website Vice. The piece, which had originally appeared in a German publication, Das Magazin, featured Cambridge Analytica (CA), a data marketing firm owned in part by billionaire Trump-backer Robert Mercer (Grassegger & Krogerus, 2017). CA was first hired during the primaries by Republican candidate Ted Cruz. When Trump won the primaries, he brought CA on board.

The Das Magazin article claimed that CA had used information from a large database of online personality questionnaires to predict and model the dominant personality profile for what it described as “220 million” people—effectively every adult American. The article also alleged that CA may have unethically integrated data from the myPersonality Project, a spin-out business from an academic study based at the University of Cambridge between 2007 and 2012.

While this might sound low key, myPersonality is, in fact, a very large repository of personal information about Facebook users. The project’s website states that, between 2007 and 2012, with users’ consent, it gathered some 7.5 million psychometric test results and downloaded 4 million Facebook profiles from a Facebook app that enabled people to find out their “personality types.” Users could take a test and give consent to myPersonality to harvest their Facebook likes (Grassegger & Krogerus, 2017; myPersonality, 2016). myPersonality was able to use test results, Facebook likes, and other data sources, such as profile pictures and descriptions, to establish correlations between user behaviors and personality types in the so-called “big five” traits model common in psychological research: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In common with many (p.266) academic projects, these data had been made available to more than 200 registered “collaborators” on the project’s website (myPersonality, 2016).

The Das Magazin article gave the impression that the psychometric method was used by Trump’s campaign to target individual voters with online messaging geared to voters’ personality types. For example, voters who scored high on neuroticism might be targeted with messages that asked them to consider what they would do if a burglar broke into their home. This caused a predictable whirlwind of commentary about the supposed previously unknown dark arts of Trump’s online campaign.

Yet the article gave no real evidence of precisely how psychometric targeting was used to mobilize voters, and the methods it described were publicly denied by CA itself (Confessore & Hakim, 2017). Instead, the practices the piece described, such as door knocking to gather data and scoring voters based on their preferred issues and whether they were likely to vote for Trump, were, by 2016, the stock-in-trade of all presidential campaigns (Karpf, 2017).

In February 2017, as Republican campaign workers began to do post-mortem interviews, it transpired that psychometric data did not play a major role in Trump’s digital campaign, and what role it did play simply derived from CA’s presence in the campaign war room, rather than a direct application of psychometric modeling (Teggart, 2017). As I show in the following, the company participated in informing the development of specific messaging for Facebook advertising and for doorstep canvassing. But the staples of this approach were demographic data, voter files, market research data, and the databases of voter preferences gathered from door-to-door and phone canvassing (Confessore & Hakim, 2017; Grassegger & Krogerus, 2017; Green & Issenberg, 2016). And in any case, the story about CA masks the broader point that Trump’s real innovation was his huge investment in Facebook advertising, irrespective of whether the ad campaign was at all informed by psychometrics.

In May 2016, as the primaries were drawing to a close, Trump went on the record in an interview with the Associated Press as saying that he believed data operations were “overrated” (Pace & Colvin, 2016). Trump said he preferred “big rallies” to generate word-of-mouth approval and free media coverage. As I showed earlier in this chapter, this was a key part of his campaign. Yet, much less noticed in Trump’s May interview were two points that revealed a subtler approach. First, Trump indicated that his campaign would be spending some “limited money” on data analysis to model voter turnout and the 270 electoral college votes he needed to beat Clinton. Second, he was putting together a deal with the Republican National Committee (RNC) to harness their voter targeting database and their volunteers on the ground (Pace & Colvin, 2016).

As the campaign went into the summer, both of these goals had been achieved. The RNC, which, between 2012 and 2014, had spent $100 million on improving (p.267) its digital infrastructure (Kreiss, 2016: 168–203; Vogel & Samuelsohn, 2016), merged with Trump’s list its email operation of more than 6 million supporters and twelve dedicated support staff. The RNC agreed to raise funds using Trump’s name, but Trump agreed to allow the RNC to keep 80 cents of every dollar. Trump also integrated other Republican email lists, from Tea Party supporter networks and the list built by Newt Gingrich during his 2012 bid for the Republican nomination (Green & Issenberg, 2016).

Trump’s field office count (207) never came close to rivaling Clinton’s (489) but, then again, Clinton’s total was itself well short of the 790 offices that Obama had established in 2012 (Darr, 2016). But there is little doubt that during the closing four months of the race, Trump significantly intensified his digital campaign. He started to use rallies to grow his email list and raise funds, not by insisting on sign-ups at the entrance door (an Obama tactic) but by insisting that attendees register in advance on his website and then confirm their attendance via mobile phone. This handed the campaign hundreds of thousands of phone numbers (Green & Issenberg, 2016). According to email marketing company Return Path, which mines the content of 2.5 million consumer inboxes, a couple of weeks before election day, Trump’s email list had grown approximately 9 percent larger than Clinton’s. At one point it had been about 20 percent larger (BusinessWire, 2016).

The target of dismissive accounts of Trump’s digital operation was Brad Parscale, a Trump family friend whose San Antonio, Texas, company worked on building websites for firms, including Trump’s own. Parscale had no experience whatsoever in political campaigns, yet Trump made him his director of digital campaigning, partly on the basis that Parscale offered to build a campaign website for just $1,500 (Green & Issenberg, 2016).

Less noticed was that from July 2016 onward, Parscale was given significant amounts of money by the Trump campaign and was asked to recruit up to a hundred new employees. Parscale also started spending significant sums—over $7 million to begin with—on Facebook ads. These ads helped boost Trump’s fundraising haul of $80 million during July alone (Lapowsky, 2016b). While it is doubtful that Parscale recruited a hundred new staff so late in the race, the campaign’s investment helped him build a digital team for Trump. And, in any case, Parscale already had sixty employees in his business (Marshall, 2016).

During August 2016, with over 100 digital staff in place, Trump further strengthened his digital expertise when he appointed as his campaign chief executive Steve Bannon, director of the right-wing news site Breitbart, an outlet that had learned how to grow its readership through Facebook shares (Green & Issenberg, 2016).

By the close of the campaign, Parscale and his team had been given $90 million. By previous campaigns’ standards, this was a substantial sum. In fact, Trump spent a greater proportion of his campaign budget on digital than Clinton (Lapowsky, 2016a).

(p.268) “Lookalikes” and “Dark Posts”

Most significant here is Trump’s innovation with Facebook advertising. On August 31, to coincide with Trump’s visit to Mexico, his campaign reportedly ran 107,000 different ads on Facebook, generating a haul that day of $5 million. The peak, however, came on October 19, the day of the third and final televised presidential debate, when the team reportedly ran 175,000 variations of their ads. The goal was to integrate Trump’s televised debate messages with a social media offensive (Lapowsky, 2016a; Mims, 2016). Lindsay Walters of the RNC stated that an average campaign day involved 40,000 to 50,000 Facebook ads (Goldmacher, 2016).

In an excellent example of how seemingly obscure technical changes in social media platforms can send ricochets through political communication practices, the new emphasis on social media ads was made possible by a significant development at Facebook. In the 2008 and 2012 campaigns, a major challenge for both parties was how to match their gathered email addresses with Facebook accounts. In 2014, however, Facebook opened up its API to other companies, such as consumer data brokers Datalogix, Experian, and Axciom. These organizations could now match Facebook accounts to full names, addresses, and phone numbers. This greatly improved the email-to-Facebook matching rate, from around 30 percent to 65–85 percent (Kreiss, 2016: 177). This enabled the RNC to start targeting Facebook advertising to members of their email list. It also helped them to use Facebook’s “Lookalike Audiences” feature. Lookalike Audiences is a platform tool that allows advertisers to use what they know about their existing audience to find other audiences that Facebook data reveal have similar preferences and attributes (Mims, 2016). The advantage to campaigns is that they can quickly grow by reaching people who are more likely to support their candidate, because campaigns know that these “lookalikes” share similar demographics and expressed interests to those who signed up for the campaign email list.

Trump also commissioned his own opinion polls from Trump Tower in New York, and, in the final month of the campaign, his digital war room in San Antonio was spending an additional $100,000 a week on their own voter surveys. These sources helped the campaign’s data scientists, including CA, build a model from email lists, voter files, and Facebook ad tests that identified 13.5 million voters in sixteen swing states whom they believed could be persuaded to support Trump. These were the so-called “shy” Trump supporters and younger, rural Republicans that CA argued traditional opinion polls and forecasts had failed to identify. CA also developed a forecasting model to help choose the best locations for rallies. These were based on the likely numbers of voters in specific locations whose data profiles indicated that they might be persuaded to vote for Trump (Green & Issenberg, 2016).

Some of this hinged on deterring likely Clinton voters from turning out. In 2008, a major objective for Obama had been expanding the electorate by (p.269) mobilizing previously non-voting African Americans (see chapter 6). Trump’s campaign tried to put this in reverse through a concerted campaign of what one of its staff openly termed “voter suppression.” To illustrate this point, it is worth quoting at length from journalists Joshua Green and Sasha Issenberg’s account of the Trump war room:

“We have three major voter suppression operations under way,” says a senior official. They’re aimed at three groups Clinton needs to win overwhelmingly: idealistic white liberals, young women, and African Americans. [During the televised debate of October 19] Trump’s invocation at the debate of Clinton’s WikiLeaks e-mails and support for the Trans-Pacific Partnership was designed to turn off Sanders supporters. The parade of women who say they were sexually assaulted by Bill Clinton and harassed or threatened by Hillary is meant to undermine her appeal to young women. And her 1996 suggestion that some African American males are “super predators” is the basis of a below-the-radar effort to discourage infrequent black voters from showing up at the polls—particularly in Florida. (Green & Issenberg, 2016)

The aim here was to integrate the themes of Trump’s media appearances with a targeted online and broadcast advertising campaign. These themes, particularly Clinton’s 1996 “super predator” remark about African American men, were reinforced by targeted radio advertising on African American stations, but more importantly by Facebook’s so-called “dark posts.”

A dark post, which Facebook also calls an “unpublished page post,” has become a common feature of advertising campaigns on the platform, though it did not attract much attention before 2016. These posts allow those with Facebook advertising accounts to use the platform’s “power editor” software to promote content by creating multiple ads targeted to the news feeds of specific groups of Facebook users. These might be based on simple demographics, for example men between the age of forty-five and sixty-five in Columbia County, Wisconsin. However, the platform also allows advertisers to search for keywords related to users’ profiles and likes. Crucially, these ads do not appear on a campaign’s Facebook page (Facebook, 2017). This feature is particularly important if a campaign is segmenting its target users into different groups and testing multiple, slightly different versions of the same ad to identify which ads produce the highest engagement rates—the so-called A/B testing that served Obama’s email campaign so well in 2008 (see chapter 6). Trump’s team experimented with different formats—video and still images, different subtitles, and text overlays. Facebook’s ad platform also now enables rapid testing of the engagement rates of dark posts and allows advertisers to run quick opinion polls to gauge user reactions. Just as important is that dark post ads have (p.270) higher click-through rates than both Facebook banner ads and so-called “boosted” and “promoted” posts because users are less likely to see dark posts as spam.

It is impossible to say with certainty whether this Facebook “voter suppression” strategy worked, or indeed how it weighed alongside the myriad variables that determine electoral success. We do know that, when compared with Obama in 2012, support for Clinton declined among African Americans, due to an increase in the numbers who did not vote. When coupled with the increase in rural, white, working-class voters who supported Trump, this small drop in turnout may have made a difference, especially in states such as Michigan, Wisconsin, and North Carolina (Ben-Shahar, 2016; Tyson & Maniam, 2016). But, as with any election, we also know that there were potentially many other reasons that Clinton was less popular than Obama with the Democratic Party’s voter base. And in some states, the introduction of new photo ID voter registration requirements has deterred ethnic minorities from voting (Hajnal, et al., 2017).

Still, the intensification of Facebook advertising and the willingness to hire companies like CA were significant departures from the campaign models of 2008 and 2012. Over recent years, Facebook has refined the suite of tools it makes available to advertisers. These have been taken up with relish by campaigns, with the assistance of paid consulting firms. This represents something of a shift away from the use of the email microtargeting that became the gold standard from 2004 onward. For campaigns, the advantage of Facebook ads are obvious. Emails often become trapped in spam filters. Dark post ads do not, and they appear in a user’s news feed alongside the rest of his or her daily diet of content. They also come with the data on user preferences that Facebook provides, in close collaboration with campaigns.

In the future, the advantages of layering psychometric data into models based on demographics and other data sources will become obvious, but only if a campaign is able to develop and test sound hypothetical correlations between personality types and support for a candidate’s messages. Given that Facebook has developed its advertising platform into a suite of tools that enable rapid and large-scale experimental testing of many thousands of ad variations, psychometric data may begin to feed into these processes. Nevertheless, this is still a highly labor-intensive approach that requires a team of staff to develop and test the ad variations, and there is little evidence that it played a major role in 2016.

The more important point is that 2016 marked a departure in other ways. From the 2004 election onward, it became a truism that campaigns raise their money online but spend it on targeted television advertising (Anstead & Chadwick, 2009; see also chapter 6; Chadwick, 2006: 162–167). At times this acted as a brake on the growth of campaigns’ digital teams. Trump moved away from this model and invested in a large digital staff to support his Facebook operation. This is a shift that signaled a rebalancing of older and newer media logics. Television was essential to Trump’s campaign, but television advertising was not; yet Facebook advertising, integrated with Trump’s television appearances, was.

(p.271) Fakes, Bots, and Hacks: Dysfunctional Hybridity

On November 5, 2016, three days before election day, the state of Colorado’s largest and most respected newspaper, the Denver Post (founded 1892), published an extraordinary article on its website. Written by reporter Eric Lubbers, the piece was titled “There Is No Such Thing as the Denver Guardian, Despite That Facebook Post You Saw.” Lubbers’s opening line was “The ‘Denver Guardian’ is not a real news source and definitely isn’t Denver’s oldest news source” (Lubbers, 2016).

The Denver Post was alerting its readers to a website, denverguardian.com, that contained an article with the headline, “FBI Agent Suspected in Hillary Email Leaks Found Dead in Apparent Murder-Suicide.” The denverguardian.com article claimed that an FBI employee who had been involved in the FBI investigation into Hillary Clinton’s use of a private email server during her time at the State Department had been found dead in a house fire in Maryland. The article was entirely fabricated. There was no such thing as the “Denver Guardian.” The site’s domain name had been registered in July 2016. There were no other news articles on the site, the address listed on the site was for a parking lot, and the image used had been taken from a random Flickr account.

And yet, the denverguardian.com article looked convincing. Aside from the presence of a few uncompleted sections of the site’s Wordpress template, to many readers clicking through from elsewhere, this could easily have passed for legitimate professional journalism. It conformed to journalism’s genre. It was not written in a sensationalist style, it was properly punctuated, had links to other sources, and (fabricated) quotations from the police and from FBI director James Comey. This made-up article had the look and feel of online journalism in the year 2016, even down to the holding line it bore to convey the thrill and immediacy of real-time online news: “this is a developing story.” The article was shared more than half a million times on Facebook, exposing large numbers of individuals to fabricated information. Over ten days, the article received 1.6 million views (Sydell, 2016).

The fake article was the product of a team of twenty-five writers employed by a company called Disinfomedia. Owned by a Los Angeles–based businessman, Jestin Coler, Disinfomedia made between $10,000 and $30,000 a month during the 2016 campaign—from the advertisements on denverguardian.com and a mini-empire of similar lookalike news sites with domains such as usatoday.com.co and washingtonpost.com.co. Many of these ads were placed with the easy-to-use Google AdSense platform that allows website owners to generate income from display ads (Sydell, 2016). This is just one example of dysfunctional hybridity in 2016.

By “dysfunctional hybridity,” I mean processes in which the interdependence among older and newer media logics may contribute to the erosion of democratic norms. The fake news of 2016 depended on a combination of media affordances (p.272) and systemic trends: the design of social media platforms and search engines, and the intense competitive pressure on professional journalism caused by the digitalization of news and the acceleration of news cycles.

Yet the problem of fake news was just one of a broader set of problems. The 2016 campaign also saw two further threats to democratic norms: the rise of technologically enabled, automated social media bot (software robot) interventions, and politically motivated hacking. These three developments—fake news, social media bots, and politically motivated hacking—are the dark frontier of the hybrid media system. They could not exist without some of the incentive structures and media affordances that now shape political communication.

Fake News as Fabricated News

There is much to be said about the fake news scandal of 2016, and this is not the place for a comprehensive analysis. In many respects, like the role played by Facebook advertising in Trump’s campaign, the influence of fake news on the outcome of the election is not easily identifiable. Indeed, this is a troubling aspect of its emergence. But let us consider how the hybrid media system enabled its rise.

When using the term fake news in the context of the 2016 campaign, it is important to be precise. Ideological bias, sensationalism, exaggeration, satire, and even simple fabrication have always been a part of the professional news industry and the internet more broadly. Equally, the argument that tabloid newspapers’ regular output of celebrity gossip is fake news does not help much in determining what was new in 2016. Matters are further complicated by the fact that, over recent years, a raft of satirical online news sites has emerged, like the Daily Currant, whose business model is based on generating ad revenues by running humorous invented articles (Rensin, 2014). And lumping together news sites that are simply ideologically biased with sites that are based on fabricated articles (Albright, 2016) may also obscure the real problem. Conservative news sites may run plenty of slanted, exaggerated stories containing material recycled from other sites, but they are not the same thing as fake news.

As we shall see, however, conservative sites were certainly important enablers of the creation of fake news. The key point is that in 2016 there was a broader systemic problem that cannot be reduced to the mere existence of a network of right-wing sites. With all of this in mind, here I define fake news as follows: the exploitation of the technological affordances and incentive structures of social media platforms, online search engines, and the broader news media industry to spread fabricated information for financial and/or political gain. Put more bluntly, the fake news problem of 2016 was a hybrid media hack. Let us unpack how it worked by examining the most startling development: the so-called Macedonian “news factory.”

During the summer of 2016, a group of young people based in the small town of Veles, which lies at the center of the Former Yugoslav Republic of Macedonia, (p.273) registered more than 150 web domain names. They then proceeded to populate these domains using free templates from the well-known blogging platform WordPress. The domain names, for example USConservativeToday.com, USADailyPolitics.com, NewYorkTimesPolitics.com, and DonaldTrumpNews.co, were designed to look like the sites of news organizations based in the United States. The youngsters in Veles proceeded to fill these sites with pro-Trump news articles that they thought would go viral. The idea was that Trump supporters would share them to signal solidarity, while Clinton supporters would share them to signal outrage.

Many of the news factory’s articles were copied and pasted, with key modifications, from a wide range of conservative and even mainstream online news sites in the United States. But many of the articles were entirely made up (Silverman & Alexander, 2016; Subramanian, 2017; Tynan, 2016). The fact that a network of conservative tabloid-style sites, such as Breitbart, Daily Caller, The Blaze, Infowars, Ending the Fed, and the Washington Examiner, had grown their audiences since the 2012 election was an important enabling force. Without this raw material, it would have been much more difficult to get the news factory up and running.

The next stage saw the fake news creators sign up to Google AdSense. AdSense is the ad syndication platform that allows website owners to make revenue based on the number of page impressions and clicks that a site receives. Once the AdSense code was embedded on each site, the fake news creators posted links to individual news articles on the Facebook pages of multiple American political groups, including popular conservative groups with hundreds of thousands of members, such as My America My Home, and Friends Who Support President Donald J. Trump. They posted the links in their own names but, to speed up the process and escape detection, they also posted using hundreds of fake Facebook profiles that they had purchased online for about 50 cents each. They even bought Facebook ads to ensure that their posts appeared in users’ feeds.

With the WordPress sites populated with pro-Trump news articles, the AdSense account set up, the site code embedded, and the links seeded to Facebook, the final step was to sit back, wait for the articles to be shared by Facebook users, and watch the AdSense revenue roll in from the page impressions and clicks generated by site visits. AdSense generates tiny amounts of money, only fractions of a cent, per page impression. But given how many shares, reactions, and comments these articles received, it was possible for the Veles youngsters to generate enough clicks to earn substantial sums—around $4,000 a month in a country where the average monthly salary is $371—mostly from American citizens eager to circulate articles in their social networks.

In some cases, the amount of engagement these articles received was truly extraordinary. For example, a fake article on the site ABCNewscom.com, “Obama Signs Executive Order Banning the Pledge of Allegiance in Schools Nationwide,” generated 2.17 million Facebook shares, comments, and reactions.5 Unlike the (p.274) Disinfomedia project that spawned the fake Hillary Clinton FBI agent story, the Macedonian fake news factory does not appear to have been driven by ideological goals. It was concerned only with generating income from the hall of mirrors enabled by Facebook’s and Google’s platforms. Of course, the factory’s role in shaping the structure of attention during the campaign may well have been ideological in its effects. This is an important avenue of future research.

To explain how and why fake news came to exist in this form, we need to situate the Veles news factory in a broader web of systemic interdependencies.

The underlying web technologies that make news sites look and function the way they do have become radically democratized. Blog template platforms such as WordPress look remarkably authoritative when placed alongside even the slickest elite media organizations’ sites, many of which, of course, also contain blogs.

The news factory was based on a mix of plagiarized bits and pieces of other articles, spliced together with added fake images and headlines, as well as outright fabrication. Much of the raw material and many of the genres of these articles stemmed from journalism produced by the network of right-wing news sites that were an important part of the conservative movement in 2016. These sites played a broader role in galvanizing support for Trump, as well as influencing the elite media agenda during the campaign (Benkler, et al., 2017).

Facebook had been important for the growth of these right-wing sites after 2008. Facebook’s advertising revenue model, as a platform, is based upon sharing, particularly sharing among family members and like-minded networks of individuals. Google’s ad syndication platform incentivizes advertisers to create content targeted to specific online audiences (in this case partisans), and that ad platform is often blind to the authenticity of the websites whose audiences it sells to its advertisers.

Fake news also worked because, in a bitterly polarized partisan struggle, supporters of Trump and Clinton wanted to generate solidarity by sharing news that they hoped would show their opponents in the worst possible light. By 2016, some 67 percent of the American public (and 44 percent of the adult population) reported getting their news from Facebook (Pew Research Center, 2016c).

In addition, the mobile internet has altered how we consume the news, transforming news cycles based on a couple of deadlines a day into political information cycles driven by constant real-time interventions by journalists, bloggers, politicians, activists, and ordinary members of the public eager, and able, to share these interventions in their own social media networks.

The new, digitally native, news organizations such as Buzzfeed, Vice News, and the Huffington Post have learned to compete in this environment by crafting articles with sensationalist click-bait headlines and attention-grabbing images that provide advertisers with evidence of user “engagement.” To shore up revenue, many reputable media organizations, such as the Washington Post, the UK’s Guardian, and CNN, to name just a few, participate in the ad syndication game themselves, hiring (p.275) “content recommendation” companies like Outbrain, Taboola, and Revcontent, which dump algorithmically generated ads and poor-quality click-bait stories “from around the web” at the bottom of news article pages. And both digitally native and pre-digital news organizations now heavily depend on Facebook for generating traffic to their websites.

Finally, Facebook and Google have grown to become vast, sprawling megaplatforms that are woven into the fabric of the web in countless ways that are often unclear to those outside the arcane worlds of online analytics and marketing.

This panoply of affordances and incentive structures—some from older media, some from newer media—is not likely to be amenable to a quick technological fix.

Social Media Bots and the Televised Debates

A second threat to democratic norms emerged in 2016 in the form of what Phil Howard and his colleagues have termed “computational propaganda” (see also Ferrara, et al., 2016; Kollanyi, et al., 2016a, 2016b). Like fake news, this is a relatively recent development at the hybrid media system’s dark frontier.

In liberal democratic contexts, the changing nature of political media events such as televised candidate debates is essential to understanding the significance of computational propaganda.6 Over the last five years, dual screening—the bundle of practices that involve integrating, and switching across and between, live broadcast media and social media—has become a well-established feature of media events (Chadwick, et al., 2017; Vaccari, et al., 2015; see also chapter 3). These new practices are reshaping political agency, and the effects are scaling up to alter the structure of communication relating to televised campaign debates. Debates are now characterized by competition, conflict, and partisanship, but also interdependence, among actors who attempt to steer the flow and meanings of debate-related news. Journalists and politicians have integrated social media into their working practices. Broadcasters commission social media sentiment analysis, real-time online polls, and present vox-pop tweets from the viewing public to provide a demotic presence in the studio and post-event “spin room.” However, the power of political staff and journalists is increasingly prone to disruption by social media user-audience networks.

As dual screened debates have become more popular, the stakes have grown. The 2016 campaign revealed that a surprising proportion of the social media discourse generated during the televised campaign debates was inauthentic, the product of automated and semi-automated social media bots whose masters sought to shape perceptions of the debate. Why does this matter?

Like the fake news phenomenon, the growth of political bots had been produced by a confluence of specific social media platform affordances and some of the incentive structures that guide political actors and journalists in the hybrid media system. People use social media to acquire information and news about the campaign, to (p.276) share information and opinions with others, and to try to influence the interpretive framing of their online followers, journalists, and politicians (Chadwick, 2011a; Chadwick, et al., 2017; Freelon & Karpf, 2014; Mascaro & Goggins, 2015). They evaluate and fact-check television presenters and try to place marginalized issues on reporters’ agendas. They create and circulate specific hashtags, send publicly accessible tweets to journalists and campaign elites, craft satirical posts in attempts to generate shareable memes and viral information cascades, and try to subvert official news framings through the use of culturally resonant affect, counterpoint, satire, exaggeration, sarcasm, and trolling. This is, in effect, a much more widely distributed, social media–enabled set of behaviors than those identified in Lang and Lang’s (2002) broadcast-era work on how television presenter commentary shapes audience perceptions.

The communicative context during and immediately after a televised debate is relatively fertile. These are long and complex events containing many policy statements and subtle behavioral cues, very few of which become salient in journalists’ reports and audience reactions. When they do become salient, it makes a difference to individuals’ responses on social media (Shah, et al., 2015). Older studies of traditional (not dual-screened) viewing of U.S. primary and presidential debates have shown that these events can affect individuals’ levels of information, attitudes toward the candidates, engagement, efficacy, and even vote choice (Benoit, et al., 2003; McKinney & Warner, 2013).

We should also consider the short-term “opportunity structure” (Chadwick, 2011b: 5–8) that now shapes engagement immediately after a broadcast event. Getting involved soon after a debate offers individuals the opportunity to influence others’ perceptions of the debate itself. Actions can be timed for when politicians, campaign workers, professional journalists, and political activists are involved in a struggle to define the candidates’ key strengths and weaknesses. However, post-debate actions like contributing to post-debate donation surges, voting in online petitions and polls, or following a party leader on Twitter are not narrowly instrumental; they are also indirect information signals designed to influence broader perceptions. These forms of engagement leave visible traces that can be read by others as signs of support for a candidate or cause. If they appear in sufficient numbers, they can influence perceptions of success or failure (Chadwick, et al., 2017).

But what if some of these social media–enabled dual screening roles are performed, not by humans, but by computers?

Bots are automated and semi-automated social media accounts that engage social media users to try to influence journalists’ and citizens’ perceptions of events, and, in the longer term, the formation of public opinion and behavior. When bots are instructed to act in concert, they produce botnets that can scale quickly, particularly if there are networks of humans organized and motivated to tweak bots’ workings as an event unfolds. Twitter is the social media platform most susceptible to bots, due to its relatively open application programming (p.277) interface, which permits all manner of applications and automated scheduling services to work with a user account. Twitter is also the most popular platform for dual screening live events.

Many bots are straightforward and useful, such as automated news aggregators that push out tweets, or responders that answer customer queries sent to companies’ Twitter accounts. Bots’ actions may, however, become problematic. This depends on the specific context and the motivations of the humans who program them. For example, a bot programmed to tweet jokes 200 hundred times a day or automatically retweet all messages containing a specific word might seem like harmless fun. Yet if deployed and orchestrated in sufficiently large numbers during an important political event, we can quickly see how tweet and retweet bots might shape understanding, most obviously by disproportionately flooding public discourse with specific messages, themes, images, and links to websites. To some extent, this is what happened during the 2016 televised presidential debates.

The first debate was on September 26. Analysis of a sample of more than 9 million tweets using 52 hashtags related to the debate shows that, across four days (debate day and the three days that followed), 20 percent of tweets about the debate came from accounts with a high degree of automation (defined as accounts that tweeted more than 200 times during the four-day period). Given that just half a percent of accounts fell into this highly automated category, it is extraordinary to consider that these accounts together generated one-fifth of the Twitter content about the first debate. The top 100 accounts by number of tweets produced an average of 500 tweets per day. This is in stark contrast with the average account, which produced only one tweet per day (Kollanyi, et al., 2016a). Clearly a great deal of bot activity occurred during the first debate, but was this likely to have assisted Clinton or Trump?

If we examine the usage of pro-Trump and pro-Clinton hashtags, it is clear that Trump enjoyed far higher levels of support than Clinton on Twitter. For the first debate, fully 39.1 percent of the debate-related tweets carried pro-Trump hashtags. This dwarfed Clinton’s total of 13.6 percent. But about a third (32.7 percent) of tweets using pro-Trump hashtags like #CrookedHillary or #MakeAmericaGreatAgain came from identifiable bots or accounts with obvious automation (defined in this case as accounts that posted more than 50 times per day). Supportive bot and automated activity was much lower for Clinton, but still reached 22.3 percent of pro-Clinton hashtagged tweets (Kollanyi, et al., 2016a). And these patterns were broadly replicated during the second presidential debate on October 9 (Kollanyi, et al., 2016b). Trump had a much larger botnet army than Clinton.

Automated Twitter activity clearly exists, and in surprisingly large, and growing, quantities. Bot activity has also become more complex and difficult to discern. Early in Twitter’s development, fake and spam accounts were relatively easy to spot. They were what became known as “eggs” because they had incomplete descriptions and no profile picture, just the stock Twitter avatar—an egg. But, by (p.278) 2016, many of the most significant bots contained profile photos (usually scraped from random websites) and profile descriptions that looked like they were written by humans. Perhaps this is because many of them were: there is a global market for astro-turfed Twitter followers, which can be bought for as little as $5 per 2,500 (MacArthur, 2016).

While the rise of debate bots presents a threat to democratic norms, we should also put this in context. For there is a second, less dramatic, set of findings from these studies. Twitter traffic peaked significantly during the debates and in the period immediately before and after each debate. Yet the vast majority of these tweets did not use obvious pro-Trump or pro-Clinton hashtags; instead, they used neutral hashtags (Kollanyi, et al., 2016a, 2016b). These tweets were still highly pertinent to the debate, they just did not contain the obvious partisan markers typical of bots and highly automated accounts. This is because these tweets were in fact posted by humans, particularly the kinds of people who generally tweet only a few times a day and, in this case, wanted to comment online during the debate. Bot-produced tweets were far outnumbered overall, but particularly in the periods closest to the live broadcast. This suggests that, while bots and automated posting of various kinds now form a key part of the systemic context of televised debates, the Twitter traffic that matters most—because it flows during the periods when publics and journalists are most attentive—is when bots play only minor roles.

Two of the most popular hashtags among Trump-supporting Twitter accounts (including bots) during the televised debates were #crookedhillary and #lockherup. These were references to one of Trump’s most outrageous campaign themes. The chant “Lock her up! Lock her up!” rang out at many of his rallies, a response to his pledge that, if elected, he would appoint a special prosecutor to investigate the claim that Clinton had contravened regulations governing conduct in office when she operated a private email server during her time as Secretary of State in the Obama administration (Hicks, 2016).

During the second television debate, Trump said, “If I win, I’m going to instruct my attorney general to get a special prosecutor to look into your situation, because there’s never been so many lies, so much deception.” Clinton responded with, “It’s just awfully good that someone with the temperament of Donald Trump is not in charge of the law in our country.” But, before Clinton could finish her point, Trump interrupted her with the line, “Because you’d be in jail” (Politico Staff, 2016). Unusually, Trump’s remark met with immediate applause from the studio audience.

Here was the spectacle of a candidate for president openly and publicly threatening his opponent with imprisonment, with the support of a section of the studio audience, and in front of 67 million television viewers (Serjeant & Richwine, 2016). Coming so close to election day, the potential damage to Clinton was obvious, and further emphasized the ongoing significance of television in the American campaign. And the situation was made much worse for Clinton when, on October (p.279) 28, with early voting well underway, FBI director James Comey announced to Congress that he was investigating a new batch of emails relevant to a closed investigation into Clinton’s private email server. These had been found on the computer of Anthony D. Weiner, a former New York congressman and husband of a Clinton campaign staffer, who was under investigation for sending explicit images to a fifteen-year-old girl (Rosenberg, 2016).

In campaign post-mortems, Clinton’s head of polling, Navin Nayak, said that the email server controversy was decisive in Clinton’s defeat, most crucially because it depressed turnout among college-educated white voters (Pengelly, 2016). But how did the context within which Trump’s (and Comey’s) extraordinary interventions come to be established? This provides our final example of dysfunctional hybridity in 2016.

Russia, WikiLeaks, and the Hacking of American Democracy

On July 4, 2016 (Independence Day), with Julian Assange still hiding in the Ecuadorian embassy in London to escape extradition to Sweden (see chapter 5), WikiLeaks published a database of 1,258 emails about the Iraq conflict. These emails had been sent to or from the private server Clinton had operated during her time as Secretary of State from 2009 to 2013.7 WikiLeaks volunteers had combed through a larger database of emails that had been released in February 2016 by the State Department in response to a freedom of information request. Crucially, WikiLeaks presented the emails in the form of a searchable online database.

Less than three weeks later, WikiLeaks published another large email leak, this time of 19,252 emails from the Democratic National Committee’s (DNC) internal computer network. The messages, which were placed online in a searchable format, came from the accounts of seven senior DNC staff, including directors of finance Jordon Kaplan, Scott Comer, Daniel Parrish, and Allen Zachary, and communications director Luis Miranda. When journalists started searching the archive on the hunt for stories, they found evidence that the DNC had breached its regulations by attempting to undermine Bernie Sanders, Clinton’s rival in the primaries (Beech, 2016).

A month before election day, WikiLeaks began releasing yet another batch of emails. Taken from the hacked Gmail account of John Podesta, the chair of Clinton’s campaign and a former White House Chief of Staff, some of the approximately 20,000 pages of messages included transcripts from private, by-invitation, speeches Clinton had given at investment banks and other Wall Street financial institutions during the early 2010s. Clinton had received payment for these speeches, and their supposedly off-the-record format (her aides had recorded her remarks) meant that her comments were less guarded than if the meetings had been fully public. None of the revelations was hugely damaging, and hard facts were in short supply. But (p.280) the emails did reveal the internal machinations of Clinton’s campaign, including its relationship with Wall Street donors and its attempts to undermine its opponents inside the Democrats (Stein, 2016).

The DNC and Podesta emails had been obtained by WikiLeaks, following a series of phishing attacks by hackers with alleged links to the Russian intelligence services. The hackers went under the names Fancy Bear and Guccifer 2.0, among others (Entous, et al., 2016). With Podesta’s Gmail account, the attack took the form of a fake email that directed Podesta to a webpage with a Google logo and a login box, into which he unwittingly typed his username and password. The webpage was a fake; Podesta had surrendered his login credentials to the hackers.

Coming, as it did, at the end of a long and bitter campaign that saw Clinton only narrowly defeat socialist Bernie Sanders in the primaries, the Podesta email leak gave journalists fresh evidence to write about Clinton’s personal character. An extended analysis of the emails for news site Vox argued, “more than anything, the Podesta emails show how Clinton is the transactional politician many have long suspected. That’s a dispiriting conclusion for some who may wish she was a pure progressive” (Stein, 2016). The message was clear enough: Clinton’s principles were in doubt. The 2016 Democratic primary campaign had opened up divisions between left and right. In the run-up to the July convention, a Pew survey found that 15 percent of those who voted for Sanders in the primaries would not be expected to vote for Clinton in the presidential election (Pew Research Center, 2016a: 24). The Podesta emails seemed to confirm these people’s suspicion, undermining the Democrats’ volunteer base and, perhaps, Clinton’s ability to turn out the vote. And, to make matters worse, in the second televised debate, Clinton was again presented with questions about how the emails revealed the contradictions between her public and private personas.

The 2016 campaign therefore witnessed a new role for WikiLeaks. Here was further evidence of its disruptive ability to morph from one repertoire to the next, all the while exploiting the power of large data leaks to destabilize and undermine the settled practices of both political and media elites. But the WikiLeaks of 2016 was a different beast from the WikiLeaks of 2010. Having mostly given up on its earlier hybrid strategy of collaborating with professional media organizations to promote transparency in the public interest, WikiLeaks had switched to dumping unredacted, often personal, information from a single individual’s email account, without any clearly defined transparency goal other than to undermine Clinton’s candidacy.

In recognition of the damage the leaks were doing to Clinton’s cause, Trump lost no opportunity to mention the hacks. At a Florida press conference on July 26, he appeared to encourage Russian hackers to hack Clinton’s emails: “I will tell you this, Russia: If you’re listening, I hope you’re able to find the 30,000 emails that are missing. . . . I think you will probably be rewarded mightily by our press” (Crowley, 2016). At a rally in Wilkes Barre, Pennsylvania, on October 10, Trump shouted, “I love WikiLeaks,” before proceeding to read from printouts of some of (p.281) the hacked Podesta emails (ReasonReport, 2016). Trump mentioned WikiLeaks 164 times during the final month of the campaign (Legum, 2017). In December 2016, with Trump as president-elect, the CIA presented to Congress the classified results of an investigation into the hacks. Their conclusion was that they believed that the Russian government had played a role in supporting the hackers, as part of a broad plan to undermine Clinton and boost Trump. Meanwhile, Julian Assange repeatedly denied that the “Russian government” was WikiLeaks’ source (Entous, et al., 2016).

The Podesta Gmail hack featured heavily in news accounts during the closing weeks of the campaign, and it came on top of the long-running saga over Clinton’s other email problem: the reports of the potential security breaches from her private server. The origins of the Clinton email saga went back several years (Hicks, 2016). Before becoming Secretary of State in 2009, Clinton had established her own email server on a computer in her home in New York State, using the mailbox hdr22@clintonemail.com. Clinton channeled all of her personal and professional correspondence through this account and chose not to use the state.gov email server that is traditionally provided to State Department personnel. Under the Federal Records Act, federal officials’ email accounts are, excepting classified materials, treated as government property, should official correspondence be requested by Congress.

Clinton stood down as Secretary of State in 2013 but did not hand over the archive of emails stored on her private server. In 2014, the State Department requested that Clinton submit these messages, as part of a push to bring the government into line with regulations. At that point, Clinton submitted 55,000 pages of work-related messages. But a few months later, in March 2015, an article in the New York Times raised questions about the security of her home server (Schmidt, 2015). Meanwhile, a House of Representatives committee investigating the 2012 terrorist attack on the US Consulate in Benghazi, Libya, in which two senior U.S. diplomatic staff were killed, had issued a subpoena requesting that all of Clinton’s emails related to Libya be made public (Schmidt, 2015). An extended period of uncertainly followed, as two investigations into Clinton’s emails—one by the State Department, the other by the FBI—kept the issue in the headlines. This tug-of-war between Republican congressional representatives and Clinton saw new batches of emails released every few months (Hicks, 2016). After fifteen months of back and forth, the conflict appeared to be over when, on July 5, 2016, FBI director James Comey ended the FBI’s investigation and cleared Clinton of any wrongdoing.

Yet, on October 28, Comey made an extraordinary intervention in the campaign. He issued a new statement informing Congress that the FBI was now investigating a new batch of emails that had been discovered as part of the Bureau’s investigation into the Anthony Weiner sex scandal. It was not until ten days later, on November 6, two days before election day, that Comey finally clarified that the new emails revealed nothing that indicated Clinton had acted improperly.

(p.282) By that stage it was too late for the Clinton campaign to repair the damage. Comey’s announcements were a gift to Trump. They gave the veneer of official respectability to the email rumors that had rumbled through the campaign since the primaries. Those rumors had been fueled by the hacks that Trump had celebrated and even encouraged, keeping the issue in the headlines. Major news outlets, including the New York Times, were eager to report on the email affair, as were right-wing online news sites such as Breitbart (Benkler, et al., 2017). And WikiLeaks was equally happy to publish the leaks from the DNC and Podesta hacks, raising the salience of Clinton’s character during key moments of the campaign, and further increasing the likelihood that professional media would keep reporting on the issue. It was a vicious circle.

Like the inauguration weekend Women’s March with which I began this chapter, the context for this hybrid confluence of forces was also global. As the evidence for Russian intelligence involvement in the Fancy Bear and Guccifer 2.0 hacks piled up (Entous, et al., 2016), the realization dawned that a foreign state had developed the power to intervene in an American election. But that power did not derive from the Russian government’s position as a unified actor exerting its influence in the international system; nor did it derive from a direct infiltration of the electoral process. Instead, it emerged diffusely and indirectly, from a set of social relations that comprised different actors with different motivations, but through whom power could flow and be used to penetrate the U.S. border. This was an assemblage of lax security, email servers, databases, fake websites, covert hacker networks, human frailty, state action, the incentives that drive professional media, and Assange’s and WikiLeaks’ desire to gain revenge on Hillary Clinton for her role in pursuing his prosecution following WikiLeaks’ 2010 leaks. It created new and surprising vulnerabilities for American democracy.


The 2016 presidential campaign saw the intensification of the hybrid media system. The main actors—the candidates, campaign workers, journalists, partisan activists, protestors, and hacker networks—actively shaped the media system in which they played such decisive roles.

Early on, Trump’s campaign gained a reputation for being disorganized and chaotic, but, as we have seen, its strategy evolved considerably during the transition from the primaries to the general election, when Trump developed an extensive Facebook advertising program. In an important shift away from the Obama years, Trump raised significant money online but did not use it to prioritize buying television advertising, spending only a fraction of what Clinton spent in 2016 (and Obama in 2012). Instead, Trump invested heavily in his Facebook advertising campaign, not least because he was able to rely on television and print media to provide (p.283) him with a great deal of earned coverage. Shifts in the underlying technologies of Facebook’s advertising platform tools—Lookalike Audiences and dark posts—enabled this rebalancing of older and newer media logics. An important aspect of this was the “voter suppression” strategy Trump’s data scientists used to target messages to deter likely Clinton supporters. While the precise influence of this approach is difficult to determine, we saw that there was some evidence that turnout fell among African American voters. It seems likely that future campaigns will intensify their focus on Facebook. Without new regulation, the use of ever more diverse sources of data alongside Facebook’s already rich behavioral data will be difficult to resist.

Trump himself proved to be adept at exploiting the power resources that the hybrid media system provides. He translated his cultural capital, accrued through his business advice books and television celebrity, into the political field, and he used his celebrity persona to enthuse his social media following. This social media visibility, in turn, enabled Trump to break through early enough in the crowded Republican primary to emerge as a contender, even in the absence of strong fundraising and opinion poll numbers. As we have seen, Twitter was particularly important for translating Trump’s illiberal Apprentice ethos of “It’s Not Personal, It’s Just Business” into the campaign. This integrated with Trump’s television and print media appearances and the spectacle of his rallies, with their racial slurs, personal insults, and the kind of audience participation that inevitably caught the attention of professional journalists eager to report on not only crowd enthusiasm but also Trump’s violations of established norms. Trump exploited the real space-internet-television nexus. Journalists, perhaps dazzled by the bizarre, responded by reporting, often critically, on these extraordinary interventions and the social media “buzz” that encircled them, further reinforcing the sense that Trump was a person to be reckoned with, whatever one’s views, and, of course, further reinforcing Trump’s claims that elite media were biased against him. But journalists were in a difficult position. To have ignored Trump would have been a dereliction of duty. Trump did not use social media to “bypass” professional media; he used social media to influence professional media. As we saw, this was not disintermediation, but intermediation.

In 2016, the hybrid media system also enabled, and was partly reshaped by, a trio of developments that served to undermine liberal democratic norms: fake news, bot activity during the televised debates, and a series of hacks and leaks that destabilized the media-politics elite and damaged Clinton’s chances. As we have seen, these phenomena were dependent upon the new logics of digital media, but, as was the case with professional journalists’ reporting of Trump, these were integrated with older media logics.

Hypercompetition’s effects on the elite news industry, the convergence upon click-bait journalism, and the power of Facebook and Google as platforms came together in the case of fake news. These developments raise serious questions about the role of platforms in the formation of public opinion.

(p.284) The intensity of the dual screening experience and the integration of social media commentary in journalists’ framing of televised media events has incentivized the use of social media bots. On a more positive note, however, there is important evidence that in 2016 any potential harm was offset by the fact that human social media activity flooded the Twittersphere during the crucial periods closest to the live broadcast, when publics and journalists were likely to be most attentive.

The new vulnerabilities created by the growth of state-sponsored, politically motivated hacking and WikiLeaks’ disregard for the norms of professional journalism converged with the motivations of congressional representatives, FBI director James Comey, journalists, and an insurgent right-wing online news network to help keep Clinton’s email server story and the DNC and Podesta Gmail hacks in the spotlight for long periods during the campaign. Clinton’s cause was not helped by the extraordinary second television debate, when 67 million television viewers saw the studio audience break debate protocol by applauding Trump’s line, “Because you’d be in jail.”

And yet, despite these troubling developments, it pays to end this chapter by recounting how it began—with two interrelated stories of counter-power from the inauguration weekend of January 2017. As we saw, a fusion of lifestyle politics, popular culture, Twitter, Instagram, and professional journalism animated the hybrid contestation of Trump’s plagiarized ceremonial cake. Fleetingly, the #cakegate hashtag became a memetic metaphor that liberal activists and professional journalists used to undermine Trump by confronting many of the symbolic resources on which the celebrity tycoon had built his persona and his campaign strategy. It was also fitting that #cakegate ended with a concrete act of political agency: a fundraising deal to channel funds to the Human Rights Campaign.

But by far the most significant story of counter-power from inauguration weekend was the Women’s March. This was a digitally enabled mobilization and a media event that was truly global in scale but which, through a mix of mediated and physical agency, condensed its force upon Washington’s National Mall. With the assistance of Reuters’ photographers, journalists on the ground, fact-checkers at the nation’s most respected news organizations, and citizen testimony, the Women’s March demonstrably undermined the White House’s claim that the size of Trump’s inauguration crowd had been the largest ever for an incoming president. The outcome was a counter-inauguration that revealed the chaos of Trump’s press strategy, while also laying down the sedimentary online activist networks upon which future mobilization would depend.


(1.) Two Wikipedia entries provide robustly evidenced accounts of the scale of the protests, including a comprehensive list of all of the locations. See “2017 Women’s March,” https://en.wikipedia.org/wiki/2017_Women's_March and “List of 2017 Women’s March locations, https://en.wikipedia.org/wiki/List_of_2017_Women's_March_locations. Retrieved January 27, 2017.

(2.) A database of 560 false claims Trump made during the campaign was compiled by the Toronto Star’s Washington Bureau chief, Daniel Dale. See https://www.thestar.com/news/world/uselection/2016/11/04/donald-trump-the-unauthorized-database-of-false-things.html#analysis. Retrieved March 13, 2017. A database of “The 319 People, Places and Things Donald Trump Has Insulted on Twitter” was compiled by New York Times reporters Jasmine C. Lee and Kevin Quealy. See https://www.nytimes.com/interactive/2016/01/28/upshot/donald-trump-twitter-insults.html. Retrieved March 13, 2017.

(3.) Ibid.

(4.) As of February 2017, Trumpshow.info had been hacked and defaced by Anonymous, the online activist network. For a discussion of Anonymous, see chapter 5.

(5.) See Buzzfeed reporter Craig Silverman’s online spreadsheet listing the top fifty fake news articles from 2016 at https://docs.google.com/spreadsheets/d/1sTkRkHLvZp9XlJOynYMXGslKY9fuB_e-2mrxqgLwvZY/edit#gid=652144590. Retrieved March 9, 2017.

(6.) The first six paragraphs of this section draw on part of a study I co-authored with Ben O’Loughlin and Cristian Vaccari (Chadwick, et al., 2017).

(7.) The Wikipedia entries for “WikiLeaks,” “2016 Democratic National Committee email leak,” and “Podesta emails” are a well-documented factual account of the 2016 hacks and leaks.