Who Shares Fake News? Uncovering Insights from Social Media Users' Post Histories
Verena Schoenmueller, Simon J. Blanchard, Gita V. Johar
TL;DR
This paper argues that social-media users’ past post histories are a rich, underused data source for understanding fake-news sharing. It combines two sampling frames (Snopes-verified fake-news sharers and Hoaxy-identified low-credibility sharers) with multiple comparison groups to extract linguistic and personality cues from users’ historical posts using LIWC, NRC lexica, and Magic Sauce Big Five, then tests predictive models that incorporate these cues alongside socio-demographics. The authors show that post-history cues—especially negative high-arousal emotions, power-related language, and existential terms—improve the identification of fake-news sharers beyond traditional demographic predictors, and they further explore interventions by linking linguistic signals to anger and power dynamics in controlled experiments. Their four-study program includes methodological innovations (authenticating Twitter accounts within surveys) and intervention tests (power-priming ads) that contribute to theory and practical misinformation mitigation, with robust cross-method validation across datasets and measures. The work highlights a promising direction for misinformation research and for designing targeted interventions, while also acknowledging limitations in causal inference and data access.
Abstract
We 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 socio-demographics, 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. Our research includes studies along these lines. In Study 1, we 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, we show that adding textual cues into predictive models enhances their accuracy in predicting fake-news sharers. In Study 3, we explore the contrasting role of trait and situational anger, and show trait anger is associated with a greater propensity to share both true and fake news. In Study 4, we introduce a way to authenticate Twitter accounts in surveys, before using it to explore how crafting an ad copy that resonates with users' sense of power encourages the adoption of fact-checking tools. We hope to encourage the use of novel research methods for marketers and misinformation researchers.