Emotions in Online Content Diffusion.

This article investigates how eight discrete emotions—surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust—expressed in online content influence the diffusion patterns of articles on WeChat, China's largest social media platform. Analyzing 387,486 articles and diffusion cascades involving over six million users, the study finds that content expressing anxiety, love, and surprise spreads more widely, deeply, and virally, while anger, sadness, and joy tend to inhibit diffusion. The research employs a novel, domain-specific emotion lexicon and advanced econometric models to control for confounding factors, revealing that emotional effects on diffusion also vary with sharers' demographics and social ties. These findings offer practical insights for content creators, platform designers, and policymakers aiming to understand or regulate information spread based on emotional expression in social networks.