What drives hateful tweets to go viral? Threat perception and Twitter engagement: The case of the COVID ‐19 pandemic

Abstract

Drawing on Intergroup Threat Theory as an analytical framework, this study analyses pandemic‐driven Sinophobic tweets to examine how threat‐laden language influences user engagement through computational text analysis. Findings showed that those conveying ideological threats generated higher engagement, whereas those involving mixed threat perceptions or moral concerns led to increased engagement only when accompanied by greater use of negative emotional language. This study extends intergroup threat theory and online hate speech literature by differentiating hateful content by the form of threat it expresses and linking these forms to user engagement. The findings provide actionable insights for policymakers and social media platforms seeking to better understand the characteristics and diffusion dynamics of hateful content and to mitigate its spread.