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

TL;DR

This paper investigates social media users' post histories to identify linguistic cues and traits associated with fake-news sharing, enhancing prediction models and suggesting intervention strategies.

Contribution

It introduces a novel approach using textual cues from user histories to predict and understand fake-news sharing behavior, including new methods for account authentication and targeted interventions.

Findings

Fake-news sharers use more anger and power words.

Textual cues improve prediction accuracy.

Trait anger correlates with sharing both true and fake news.

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.