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Introduction

Social media has experienced an exponential growth since the 2000s (Edosomwan et al., 2011), with sites like Facebook and Instagram boasting well over one billion active users as of 2021 (Tankovska, 2021a). Social media offers a convenient platform for reaching global audiences, lending itself to the rapid transmission of information - be it authentic or fake. The proliferation of ‘fake news’ has been propelled by social media, with recent research highlighting that an overload of information - as is typical of social media platforms - reduces one’s capacity to evaluate source authenticity (Qui et al., 2017), while simultaneously increasing the likelihood of fake news sharing (e.g., Bermes, 2021). Moreover, although 84% of American adults rate themselves as ‘confident’ in discriminating between fake and real news, 75% mistakenly categorise fake news headlines as accurate (Silverman & Singer-Vine, 2016). As such, social media platforms provide optimal conditions for the spread of false information.

The ‘fake news’ phenomenon is inherently related to politics, with the 2016 US Presidential Election serving as a tipping point. The internet was rife with false information: investigations reported that over 100 sites featuring falsified pro-Trump content could be traced to Macedonia (Silverman & Alexander, 2016), and that in the three months prior to Donald Trump’s election, fake news stories concerning Trump were shared 30 million times on Facebook (Allcott & Gentzkow, 2017). This has fed into hypotheses that the dissemination of fake information via social media catalyses political polarisation (Tucker et al., 2018), while simultaneously diminishing trust in mainstream sources of information (Ognyanova et al., 2020).

Recent analyses have attested to how “toxic” (Al-Rawi, 2021, pg. 276) social media platforms have become to political discourse (Al-Rawi, 2021). Although it has been argued that social media can be used to challenge and change people’s political views by increasing exposure to diverse opinions and sources of information (e.g., Shirky, 2011), the algorithms employed by platforms such as Facebook and Instagram filter information that deviates from the beliefs of the consumer (based on their interactions), creating echo-chambers (Pariser, 2012). There is an abundance of research documenting group polarisation (e.g., Moscovici & Zavalloni, 1969), and one might, based on the presented evidence, hypothesise about how the worldwide increase in the use of social media (Tankovska, 2021b) is related to increased political extremism (e.g., Bright, 2018).

It is important that we understand the hazards posed by the ongoing proliferation of fake news on social media platforms - which are being used more frequently each year - to political discourse and polarisation. In the following literature review, we seek to assess how people engage with fake versus real news on social media; how they perceive it; and the impact that this has on their political opinions and behaviour. It is critical that we understand how the modern political discourse is shaped by fake news, and that we conceive of ways to disrupt its distribution on social media.

II. Literature review: This brief explores the literature covering three key areas: user exposure to fake versus real news on social media; how users perceive the fake news they encounter; and the effect that exposure to misinformation has on political opinions and behavior.

a. Exposure to fake news versus real news on social media (note that due to the subject of this subsection, the majority of studies utilise content analysis. Although an experiment-based study might provide more primary data, I believe that the use of content analysis is most appropriate for answering questions regarding the spread of misinformation on social media because of its high ecological validity.)

i. Content analysis of 6 popular hyper-partisan Facebook pages (three left-wing and three right-wing) revealed that 12.3% of Facebook posts from the right-wing pages presented “mostly false” information; an additional 25.4% of posts comprised a mixture of true and false information. By comparison, 5% of posts on left-wing pages were “mostly false”, and 14% provided both true and false information (Silverman et al., 2016a).

ii. In 2016, people engaged significantly more with fake content on Facebook than they did with content from major news outlets, with the 20 top-performing fake news stories receiving 8,711,000 shares, reactions, and comments on Facebook alone. This was significantly more than the top 20 real news stories from legitimate news sites (Silverman et al., 2016b).

iii. A study by researchers at MIT tracked the trajectories over 126,000 stories spreading on Twitter, and reported that fake news stories travelled more rapidly than real news (Vosoughi, Roy, & Aral, 2018)

iv. An algorithmic model applied to data from two social media sites (Weibo and Twitter) showed that fake news penetrates a wider network (especially in the first 5 hours after posting) than does real news such that the ‘characteristic distance’ between reposters is longer for fake news, thereby reaching more ‘layers’ (defined as a reposting whose re-posters have the same distance from the creator) (Zhao et al., 2020).

v. Data collection and analysis of Facebook users’ interactions with content from 69 public pages (32 documenting conspiracy theories; 35 scientific news; and 2 toll pages) showed that content-selective exposure drives content diffusion, as well as the formation of echo-chambers that increase the homogeneity of information to which an individual is exposed - promoting exposure to misinformation (Del Vicario et al., 2015).

vi. Survey data and web-traffic history were analyzed and showed that 1 in 4 Americans visited a fake news site during the 2016 election. However, this fake-news consumption was dominated by Trump supporters visiting sites featuring Pro-Trump content, with most visits to fake news websites attributable to just 10% of highly conservative Americans (Guess, Nyhan & Reifler, 2020)

b. How users perceive fake news online

i. A recent experiment examining the relationship between engagement with fake news and filter bubbles reported that participants (N = 1749) rated fake stories as more believable if they were assigned to a ‘filter bubble’ condition - in which they were only exposed to stories consistent with their worldview (Rhodes, 2021).

ii. An online survey experiment found that participants (N = 1980) were more likely to share news reports that came from real sources, than from fake sources. Serial exposure to fake reports consistent with their political beliefs increased participants’ belief in their veracity, as well as the likelihood of them sharing those reports on Facebook. This effect was not significant for real news reports (Bauer & Clemm von Hohenberg, 2020).

iii. A study by Pennycook, Cannon and Rand (2017) found that repeated exposure to stories - real or fake - increased participants’ (N = 409) likelihood of perceiving it as credible, even when the headline was inconsistent with their political ideology. Furthermore, this effect was robust to explicit warnings about the accuracy of the news story, and persisted one week later (N = 940) (Pennycook et al., 2017)

iv. Content analysis of 14 million messages spreading over 400,000 articles on Twitter between 2016 and 2017 showed that, not only was false news more novel than the truth, but people were more likely to share novel information. People expressed more surprise and disgust to false rumours than to truth, supporting the notion that fake news propagates through social media because it is (a) more likely to capture the attention of the consumer and (b) elicits arousal (Aral, 2020).

v. Furthermore, an online survey reported that participants (N = 409) who indicated heightened emotionality prior to exposure to a mix of fake and real news reports were more likely to rate fake news stories as credible (Martel, Pennycook & Rand, 2020).

vi. Another experiment by Pennycook and colleagues (2020) highlighted that explicit warnings about credibility elicited an implicit truth effect, whereby any headlines that were not accompanied by a warning were subsequently perceived by participants (N = 5271) as more accurate - even if they were false (Pennycook et al., 2019).

c. What is the impact of social media on political behaviours and opinions? (Literature is generally scarce regarding direct impact)

i. Frequent Twitter users, self-identifying as Democrat (N = 901) or Republican (N = 751), were asked to follow a Twitter bot that would expose them to messages of the opposite political stance; participants completed a survey throughout and after a one month period. Results indicated that Republicans’ (those exposed to a liberal bot) opinions were significantly more conservative at the end of the trial period, while there was only a statistically-insignificant increase in liberal beliefs in Democrats (Bail et al., 2018)

ii. Content analysis of political beliefs expressed over time on Twitter according to a quantitative model, showed that a sample of nationally-representative users (across Germany, Spain and the USA) were part of ideologically diverse networks, which was linked to dynamic moderation of political views (Barberá, 2015).

iii. Yarchi and colleagues (2021) collected all posts and messages on Facebook, Twitter and WhatsApp that pertained to a stabbing associated with the Israel-Palestinian conflict. Their analysis indicates that political polarisation is not a “unified phenomenon” (pg. 114). On Twitter, people engage in homophilic interactions which exacerbate positional polarisation, resulting in increased hostility to individuals expressing diverging beliefs. WhatsApp can, on the other hand, de-polarise over time even after initial negativity. Facebook increased in heterophilic interactions over time, but many accounts stopped participating in public discussions preventing further analysis (Yarchi, Baden, & Kligler-Vilenchik., 2021).

iv. Regression analyses of survey data revealed that increased use of social media was a significant predictor of a respondent endorsing fake rumour about President Obama in the 2012 election, although the effect size was small (Garrett, 2019)

v. Relatedly, an analysis of fake news exposure and voting data pertaining to elections in Italy in 2013 and 2018 highlighted that exposure to fake news favouring populist parties was positively correlated with support for that party. However, the evidence suggests this effect was primarily explained by prior support for populist parties (‌Cantarella, Fraccaroli, Volpe, 2020)

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