Social Media and Fake News in the 2016 Election

By Hunt Allcott, Matthew Gentzkow. 2017

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

Automatic Detection of Generated Text is Easiest when Humans are Fooled (2019, background): “It propogates quickly (Vosoughi et al., 2018), sets political agendas (Vargo et al., 2018), inﬂuences elections (Allcott and Gentzkow, 2017), and undermines user trust (Wang et al., 2012; Song et al., 2015).”
Automated Fact Checking: Task Formulations, Methods and Future Directions (2018, background): “As many of the barriers required to publish information are effectively removed through the advent of social media, it is possible for content written by any individual or organization to reach an audience of hundreds of millions of readers (Allcott and Gentzkow, 2017).”
Fake News Detection Using Machine Learning Ensemble Methods (2020, background): “A number of studies have primarily focused on detection and classiﬁcation of fake news on social media platforms such as Facebook and Twitter [13, 14].”
The Reception of Fake News: The Interpretations and Practices That Shape the Consumption of Perceived Misinformation (2019, background): “It has largely resorted to cognitive and social networks frameworks, and from computational methods, which have been able to illustrate the spread of falsehood online (Allcott and Gentzkow 2017; Guess, Nagler, and Tucker 2019; Pennycook and Rand, 2019a; Tandoc Jr, Wei Lim, and Richard Ling 2018).”
Nudge Effect of Fact-Check Alerts: Source Influence and Media Skepticism on Sharing of News Misinformation in Social Media (2020, background): “By the same token, mistakes made by fact-checkers, debates about “who becomes the arbiter of truth” (Allcott & Gentzkow, 2017, p. 233), and the alleged activist agenda of fact-checkers (Haigh et al., 2018) cast aspersions on fact-checking intentions and raise doubts about the effectiveness of such…”
What is AI Literacy? Competencies and Design Considerations (2020, background): “How Should AI Be Used? There are many ethical questions surrounding how AI should be used, and there has been growing concern surrounding issues such as AI’s effect on the job market [57], bias and discrimination in AI [7,24,99], and AI-related data privacy scandals [119].”
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection (2020, unknown): “elections and security threats [37], [38].”
A survey on fake news and rumour detection techniques (2019, methodology): “Authors in [4] thoroughly studied the subject, reporting interesting findings: during the campaign voters were exposed to higher number of pro-Trump than pro-Clinton articles.”
The diffusion of misinformation on social media: Temporal pattern, message, and source (2018, unknown): “While some research (Allcott & Gentzkow, 2017) has found that t e impact of false news stories is only a fraction of television campaign ads, other studies showed that, nonetheless, the exposure to misinformation could have real consequences such as voting decisions (Weeks & Garrett, 2014) and…”
“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content (2019, background): “These assertions typically lack evidence and are based on appeals to emotion and preexisting attitudes (Allcott & Gentzkow, 2017; Digital Resource Center, 2017).”
Which of These Things Are Not Like the Others? Comparing the Rational, Emotional, and Moral Aspects of Reputation, Status, Celebrity, and Stigma (2019, background): “Because social media is largely devoid of editorial oversight and fact-checking (Allcott & Gentzkow, 2017) and the users of social media platforms are not bound by the ethics of journalism, they do not feel obliged to provide neutral accounts or ensure the accuracy of the information they provide…”
A Survey of Fake News (2020, background): “…date, no universal definition is provided for fake news, where it has been looked upon as “a news article that is intentionally and verifiably false” [Allcott and Gentzkow 2017; Shu et al. 2017] (deceptive news), or “a news article or message published and propagated through media, carrying false…”
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online (2019, background): “For instance, more than 100 million Americans were exposed to political ads paid for by Russian agents that explicitly sanctioned and highlighted bad behavior of political candidates and political groups (Allcott & Gentzkow, 2017; Timberg, Dwoskin, Entous, & Demirjian, 2017).”
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (2018, background): “For example, within the final three months of the 2016 U.S. presidential election, the fake news generated to favor either of the two nominees was believed by many people and was shared by more than 37 million times on Facebook [1, 7].”