Emotions in Online Content Diffusion
Yifan Yu, Shan Huang, Yuchen Liu, Yong Tan

TL;DR
This study investigates how negative emotional expressions like anxiety, sadness, anger, and disgust influence the diffusion patterns of online content on social media, revealing that certain emotions promote wider spread while others limit it.
Contribution
It introduces a novel computational approach combining social network theories and a new emotion lexicon to causally analyze the impact of negative emotions on content diffusion.
Findings
Anxiety increases content spread and virality.
Anger and sadness decrease cascade size and breadth.
Negative emotions influence diffusion differently based on individual traits and social ties.
Abstract
Social media-transmitted online information, which is associated with emotional expressions, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses and use a computational approach to investigate how emotional expressions, particularly \textit{negative discrete emotional expressions} (i.e., anxiety, sadness, anger, and disgust), lead to differential diffusion of online content in social media networks. We rigorously 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…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Social Media and Politics
MethodsDiffusion
