Emotions in Online Content Diffusion

Abstract

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.This study examines the impact of discrete emotional expression (i.e., expression of anxiety, sadness, anger, disgust, love, joy, surprise, and anticipation) on the differential diffusion of online content in social media networks. We conducted an analysis on a random sample of 387,486 online articles and their corresponding diffusion cascades, involving more than six million unique individuals, on a major online social networking platform. Our investigation focused on the relationships between discrete emotional expression and the diffusion of online articles, specifically the structural properties of diffusion cascades, such as size, depth, maximum breadth, and structural virality. We employed various econometric model specifications, and our results robustly demonstrate that articles expressing higher levels of anxiety, love, and surprise reach a larger number of individuals and diffuse more deeply, broadly, and virally. In contrast, expression of anger, sadness, and joy exhibit the opposite effect. Additionally, we find that articles with different emotional expression tend to spread differently based on individual characteristics and social ties. Our findings offer valuable insights into the diffusion and regulation of online content from the perspectives of emotional expression and social networks.History: Xiaoquan Zhang, Senior Editor; Jingjing Zhang, Associate Editor.Funding: This work was supported by the Seed Fund for Basic Research for New Staff by the University of Hong Kong [Project 104006417].Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.0611 .