Who Shares Fake News? Uncovering Insights from Social Media Users’ Post Histories

The authors propose that social media users’ own post histories are an underused yet valuable resource for studying fake-news sharing. By extracting textual cues from their prior posts and contrasting their prevalence against random social media users and others (e.g., those with similar sociodemographics, political news-sharers, and fact-check sharers), researchers can identify cues that distinguish fake-news sharers, predict those most likely to share fake news, and identify promising constructs to build interventions. This article includes studies along these lines. In Study 1, the authors explore the distinctive language patterns of fake-news sharers, highlighting elements such as their higher use of anger and power-related words. In Study 2, they show that adding textual cues into predictive models enhances their accuracy in predicting fake-news sharers. In Study 3, the authors explore the contrasting role of trait and situational anger and show trait anger is associated with a greater propensity to share both real and fake news. In Study 4, they introduce a way to authenticate Twitter accounts in surveys and use it to explore how crafting an ad copy that resonates with users’ sense of power encourages the adoption of fact-checking tools. The authors hope to encourage the use of novel research methods for marketers and misinformation researchers.