It can be hard to tell to tell what direction a revolution is moving in when you’re standing in the middle of one. Such is the case as we look at the last four years of politics in the United States and the influence of social media platforms such as Twitter and Facebook upon the choices that people make as to who they feel is best-suited to serve their interests in elected office. Traditional gatekeepers of information are pushed aside as new methods of communication bring politicians and their messages closer to voters, but also allow for the proliferation of falsehoods and conspiracies on a heretofore unprecedented scale. New opportunities appear for people to become engaged in the civic arena at the same time that online algorithms are possibly adding to the polarization of the electorate. What is certain, however, is that the appearance of new technologies has changed forever how Americans define politics and politicians.
l. A New Path to the Presidency
It’s unlikely that Donald J. Trump would have become president in an earlier media era. There have always been presidential candidates outside of the two major parties, of course, who ran for office with varying degrees of seriousness: George Wallace, Pat Paulson, Ross Perot, Vermin Supreme. With the exception of Perot, none had any discernible impact on the outcome of any election, most being unable to break through the wall-to-wall political coverage that the news media lavished on “viable” candidates from the major parties. None of them, however, ran in the era of Twitter.
Trump’s personality, vocabulary and style of campaigning were perfectly geared to what Brian Ott called the three key features of Twitter: simplicity, impulsivity and incivility (Ott, 2017). Simplicity and repetition were the key to Trump’s abrasive message of national dysfunction, delivered in Twitter’s 140-word bursts, and this ability to bypass traditional media with the new technology ensured that he would be able to reach his supporters over the heads of the usual gatekeepers and eventually force the media and his political rivals to deal with him on his terms and in his own “political language,” which tended to revolve around themes of social division usually eschewed by more mainstream politicians (Nechushtai & Lewis, 2019; del-Fresno García & Daly, 2019, p. 69). Trump’s impulsive Twitter-fueled feuds with his rivals, both in the Republican primary and the general election, fed into the traditional media’s attempts to provide “balanced” coverage, allowing him to drive the news cycle with each new tweet and dominating coverage in a media environment that was rapidly becoming a hybrid of both traditional outlets such as television and newspapers and newly emergent social media such as Twitter and Facebook (Massing, 2018; del-Fresno García & Daly, 2019). His lack of traditional political decorum, insulting opponents and detractors and floating outlandish conspiracy theories, furthered his reach in a medium where the most emotionally-volatile messages tend to travel the furthest via larger numbers of retweets (Ott, 2017). In short, if a mad scientist wanted to construct a political candidate best able to take advantage of the new media landscape, that candidate would be Donald Trump.
II. The Fallacies of Facebook and Twitter
But Twitter wasn’t the only new media platform that gave Trumpian politics an advantage. Eighty percent of the Trump campaign’s advertising dollars were spent on Facebook, while his opponents continued to rely on media outlets such as television and radio (Halpern, 2018). Some of those dollars were spent on the services of Cambridge Analytica, a “big data” firm that used information gathered from Facebook users’ profiles – without their knowledge – to construct “psychographic” profiles that would micro-target political ads towards users based on emotional triggers such as fear and prejudice, both to which Trump appealed (Halpern, 2018). Such a manipulative utilization of technology may sound like something out of one of Philip K. Dick’s dystopian sci-fi novels, or an episode of “Black Mirror,” but its use will probably only increase as political candidates following the Trump model battle to reframe issues, language and even voters’ perception of reality itself in a manner that aids their ascension to power (del-Fresno García & Daly, 2019; Halpern, 2018).
Trump’s style of white-hot rage over social slights both real and imagined created an “emotional contagion” among his supporters, which is amplified by his use of social media (Ott, 2017). His ability to traffic in outright falsehoods and conspiracies – despite attempts by the traditional media to debunk them – was aided, however, by a trick of human psychology that was magnified by social media platforms such as Facebook. While studies have differed on the overall impact of “filter bubbles” – self-contained cantons of the politically like-minded created by Facebook and other social media platform’s sorting algorithms – there’s evidence that exposing people to political opinions different than their own via Twitter results in a “backfire” effect, in which motivated reasoning only causes them to make a further emotional investment in their pre-existing beliefs (Beaufort, 2018; Sunstein, 2001; Bail et al., 2018). This factor, along with users’ tendencies toward “confirmation bias,” or seeking out news and information that reinforce pre-existing beliefs, made it hard for traditional journalists to dispel falsehoods with facts (Tufekci, 2017). It would seem that on the whole, social media platforms tend to create the kind of reactions in users that are most useful to those in politics who favor emotion over reason.
III. Rise of the Robots
These emotional reactions in users are furthered by the use of non-human actors known as “bots”, computer programs that perform simple tasks over and over again, and can be programmed to retweet messages on Twitter. In a political context, they’re often used to spread misinformation to influence public opinion and further political polarization (Howard & Kollanyi, 2016). The bots exploit the fact that 62 percent of Americans get their news from social media, and that disinformation is most likely to have an effect on younger users and those with less education (Ott, 2017; Humprecht, 2018). Many users may have a hard time telling the difference between the activities of bots and actual humans, leading them to retweet information from false or misleading news sources (Howard & Kollanyi, 2016; Humprecht, 2018). Bots can also be used in more sinister ways, such as coordinating barrages of online threats directed at critics of regimes or political ideologies, often from anonymous Twitter accounts, or overwhelming websites with “DDOS” (denial of service) attacks (Tufekci, 2017; Howard & Kollanyi, 2016). The result is that voices of dissent are often driven offline and silenced, or drowned out by networks of bots deliberately spreading misinformation from a government, non-state actor or politician (Tufekci, 2017).
lV. A New Battlefield
All of these factors, from the decline of traditional media gatekeepers to the rise of artificially-generated disinformation, can threaten democracy through the empowering of demagogues and the creation of a sense in the general public that election outcomes are increasingly determined not by who can best articulate solutions to the issues, but by who can best use the tools of the new media to manipulate voters, thereby casting the legitimacy of the whole process in doubt (Ott, 2017; Halpern, 2018). It’s important, however, not to fall into the trap of “technodeterminism” – simply because the architecture of Twitter and other social media platforms may benefit a candidate with the personality and ethics of Donald Trump doesn’t mean that all future political candidates will be like him, or even have to be like him (Tufekci, 2017). In politics as in war, once one side gains a technical or tactical advantage, the other side is quickly working to adapt the new development to their own ends or invent a new weapon that will return the advantage to their side. This may be a comforting thought to those whose political sympathies lie on the opposite end of the spectrum from Donald Trump, but it does little to address the new age of politics in which we live, one in which voters can never be sure just how true the information that they’re presented with is, or to just what the purpose is of those presenting it. The answer may well come from new generations of activists and leaders, raised in the digital era and fully aware of its pitfalls, who can use the technology in a positive way to build community and better both the online and offline worlds.
References
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., … Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221. https://doi.org/10.1073/pnas.1804840115
del-Fresno García, M., & Daly, A. J. (2019). Limits for the Political Communication through large Online Platforms: from The Caste to The Plot. Límites Para La Comunicación Política Desde Las Grandes Plataformas Sociales de Internet. Un Caso de Estudio: De La Casta a La Trama., (165), 65–81. https://doi.org/10.5477/cis/reis.165.65
Halpern, S. (2018). Mind Games. New Republic, 249(11), 16–25.
Howard, P. N., & Kollanyi, B. (2016). Bots, #Strongerin, and #Brexit: Computational Propaganda During the UK-EU Referendum. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2798311
Humprecht, E. (2018). Where ‘fake news’ flourishes: a comparison across four Western democracies. Information, Communication & Society, 1–16. https://doi.org/10.1080/1369118X.2018.1474241
Massing, M. (2018). Journalism in the Age of Trump. Nation, 307(4), 12–18.
Ott, B. L. (2017). The age of Twitter: Donald J. Trump and the politics of debasement. Critical Studies in Media Communication, 34(1), 59–68. https://doi.org/10.1080/15295036.2016.1266686
Tufekci, Z. (2017). Twitter and tear gas: the power and fragility of networked protest. New Haven ; London: Yale University Press.
Photo by Daniel Bayer