Emotion Impact and Contagion Mechanism of Misinformation: A Network-Based Risk Analysis.

The proliferation of online misinformation poses severe societal risks, including public health crises and political polarization, with emotional manipulation serving as a key driver of its spread. However, traditional metrics fail to capture the nuanced role of emotions and coordinated amplification in misinformation networks. To address this gap, this study examined emotional contagion dynamics in misinformation diffusion across heterogeneous actor networks on social media. It employed machine learning-based actor classification, Exponential Random Graph Models (ERGM), and time-series analysis on retweet, reply, and quote networks. ERGM disentangled emotion-specific sender and receiver effects and temporal lagged analyses traced dynamic emotional propagation across actor categories. Key findings revealed that anger and sadness exhibited sender-dominated spread in retweet networks but receiver-driven dynamics in replies. Moreover, astroturfing actors strategically gatekept emotional flows by occupying high-betweenness positions to amplify polarization. Triadic closure effects further strengthened in-group emotional reinforcement. Taken together, these results demonstrate that emotional manipulation in misinformation ecosystems amplifies digital information risks, with astroturfing actors boosting negative emotions to trigger self-reinforcing contagion cycles. Consequently, this study underscores the need for emotion-aware risk assessment tools and context-sensitive moderation strategies. By pioneering a network-sensitive framework to quantify the risks of emotional manipulation, this work offers actionable insights for platform governance and misinformation research.