Emotions in Online Content Diffusion

Which emotions make a post go viral, and which hold it back? Analyzing 387, 000 news articles and the sharing paths of more than six million users on WeChat—China’s super-app for social media—we map how eight discrete emotions drive information diffusion. Econometric models show that content expressing anxiety, love, or surprise reliably travels farther; reaches more unique people; and forms deeper, broader, more viral cascades, whereas anger, sadness, and even joy dampen propagation. Diffusion also varies with who shares the content and how strongly sharers are connected, underscoring the importance of audience- and tie-specific strategies. For practitioners, framing messages around constructive uncertainty (anxiety), prosocial appreciation (love), or unexpected insight (surprise) can amplify reach, whereas caution is warranted when leveraging anger. For policymakers and platforms, monitoring anxiety- and surprise-laden posts enables early intervention, and transparency audits must weigh the unequal amplification power of specific emotions when evaluating recommender algorithms and influence operations.