Fake news on Twitter during the 2016 U.S. presidential election

By Nir Grinberg, Kenneth Joseph, Lisa Friedland, B. Swire‐Thompson, David M. J. Lazer. 2019

1,501 citations recorded by the source corpus.

Finding facts about fake news There was a proliferation of fake news during the 2016 election cycle. Grinberg et al. analyzed Twitter data by matching Twitter accounts to specific voters to determine who was exposed to fake news, who spread fake news, and how fake news interacted with factual news (see the Perspective by Ruths). Fake news accounted for nearly 6% of all news consumption, but it was heavily concentrated—only 1% of users were exposed to 80% of fake news, and 0.1% of users were responsible for sharing 80% of fake news. Interestingly, fake news was most concentrated among conservative voters. Science, this issue p. 374; see also p. 348 A small proportion of voters share and are exposed to the majority of online fake news. The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “While past studies show that such content is rarely shared, its’ consumption is concentrated within certain groups like older users (Grinberg et al., 2019; Guess et al., 2019).”
Countering Misinformation and Fake News Through Inoculation and Prebunking (2021, background): “…recent reports that susceptibility to misinformation is greater on the populist right and among strong conservatives than the political left (Grinberg et al., 2019; Guess, Nyhan, et al., 2020; Guess et al., 2019; Guess, Lockett, et al., 2020; Ognyanova et al., 2020; van der Linden,…”
Partisan Polarization Is the Primary Psychological Motivation behind Political Fake News Sharing on Twitter (2020, methodology): “As a robustness test, supplementary analyses discussed below group the fake news sources according to a more fine-grained coding scheme proposed by Grinberg et al. (2019) that distinguishes among three classes of fake news sources differing in their likelihood of publishing misinformation.”
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020, background): “…nearly 6% of all news consumption, where 1% of users are exposed to 80% of fake news, and 0.1% of users are responsible for sharing 80% of fake news (Grinberg et al., 2019), and democratic elections are vulnerable to manipulation of the false or unveriﬁed claims on social media (Aral and Eckles,…”
Educative Interventions to Combat Misinformation: Evidence from a Field Experiment in India (2020, background): “A consistent finding from political science research on misinformation is that older adults are most likely to engage with fake news sources (Grinberg et al. 2019).”
Moderating with the Mob: Evaluating the Efficacy of Real-Time Crowdsourced Fact-Checking (2021, background): “Additionally, while source credibility is used as a proxy for article veracity in a number of studies (e.g., Grinberg et al. 2019; Guess, Nagler, and Tucker 2019; Allen et al. 2020), Allcott and Gentzkow (2017) estimate that only roughly half of the articles from low-credibility domains are indeed…”
Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2022, background): “In particular, human-written disinformation(2), which is often used to manipulate certain populations, had catastrophic impact on multiple events, such as the 2016 US Presidential Election (Grinberg et al., 2019), Brexit (Bastos and Mercea, 2019), the COVID-19 pandemic (van Der Linden et al.”
Political Effects of the Internet and Social Media (2020, neutral): “…their political news from mainstream media outlets, and only some extreme-right-leaning users’ news feeds were dominated by false stories.11 Guess, Nagler and Tucker (2019) also study the characteristics of the sharers of false news on Facebook during the 2016 U.S. presidential election campaign.”
The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes (2019, background): “…to firm-level issues (e.g., firm-consumer relationships, firm reputation) and societal issues (e.g., social media as a surveillance tool, lack of trust in media and democratic institutions) (e.g., Allcott and Gentzkow, 2017; Grinberg et al., 2019; Martin, 2018; Martin et al., 2017; Trottier, 2016).”
The Debunking Handbook 2020 (2020, neutral): “Worldview can affect what content people choose to consume 76, 77, 78 .”
Priming critical thinking: Simple interventions limit the influence of fake news about climate change on Facebook (2019, background): “…Kiesler, 2018), its reach and potentially deleterious influence has been exacerbated by its prevalence on a wide range of social media platforms, and by its purported role in influencing voters during the 2016 federal election (Allcott and Gentzkow, 2017; Grinberg et al., 2019; Guess et al., 2018).”
The Reception of Fake News: The Interpretations and Practices That Shape the Consumption of Perceived Misinformation (2019, background): “They suggested that the effects might not have been large, as its diffusion occurred in tightly connected clusters that did not necessarily influence the overall social media sphere (Allcott and Gentzkow 2017; Bovet and Makse 2018; Grinberg et al. 2019; Guess et al. 2018; Nelson and Taneja 2018).”
Fake news, disinformation and misinformation in social media: a review (2023, background): “…political science (Valenzuela et al. 2022; Bringula et al. 2022; Ricard and Medeiros 2020; Van der Linden et al. 2020; Allcott and Gentzkow 2017; Grinberg et al. 2019; Guess et al. 2019; Baptista and Gradim 2020), health science (Alonso-Galbán and Alemañy-Castilla 2022; Desai et al. 2022; Apuke…”
Fake news game confers psychological resistance against online misinformation (2019, result): “…some small variation in our results in line with previous research, such as that the elderly (Guess, Nagler, and Tucker, 2019) and conservatives (Grinberg et al., 2019; Guess, Nagler, and Tucker, 2019) may be more susceptible to fake news, we find no (practically) meaningful differences in…”