Emotions in Online Content Diffusion

Abstract

Social media-transmitted online information, which is associated with emotional expression, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses and use a computational approach to investigate how emotional expression, particularly negative discrete emotional expression (i.e., anxiety, sadness, anger, and disgust), leads to differential diffusion of online content in social media networks. We quantify diffusion cascades' structural properties (i.e., size, depth, maximum breadth, and structural virality) and analyze the individual characteristics (i.e., age, gender, and network degree) and social ties (i.e., strong and weak) involved in the cascading process. In our sample, more than six million unique individuals transmitted 387,486 randomly selected articles in a massive-scale online social network, WeChat. We detect the expression of discrete emotions embedded in these articles, using a newly generated domain-specific and up-to-date emotion lexicon. Different model specifications are used to robustly demonstrate the relationships between negative discrete emotions and online content diffusion. We find that articles with more expression of anxiety spread to a larger number of individuals and diffuse more deeply, broadly, and virally. Expression of anger and sadness, however, reduces cascades' size and maximum breadth. We further show that the articles with different degrees of negative emotional expression tend to spread differently based on individual characteristics and social ties. Our results shed light on content generation, diffusion, and regulation, utilizing negative emotional expression.

Keywords: Information Diffusion, Online Content, Emotion Detection, Social Networks, Social Media

JEL Classification: M15,M31