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

Area of Science:

• Social Sciences
• Computer Science
• Network Science

Background:

• Online misinformation poses significant societal risks, including public health crises and political polarization.
• Emotional manipulation is a key driver in the spread of misinformation.
• Traditional metrics fail to capture the complex role of emotions and coordinated amplification in misinformation networks.

Purpose of the Study:

• To examine emotional contagion dynamics in misinformation diffusion across heterogeneous actor networks on social media.
• To develop a network-sensitive framework for quantifying the risks of emotional manipulation in online misinformation.
• To offer actionable insights for platform governance and misinformation research.

Main Methods:

• Machine learning-based actor classification.
• Exponential Random Graph Models (ERGM) to analyze emotion-specific sender and receiver effects.
• Time-series analysis on retweet, reply, and quote networks to trace dynamic emotional propagation.

Main Results:

• Anger and sadness showed sender-dominated spread in retweets but receiver-driven dynamics in replies.
• Astroturfing actors strategically amplified polarization by controlling emotional flows.
• Triadic closure effects reinforced in-group emotional responses, strengthening contagion cycles.

Conclusions:

• Emotional manipulation in misinformation ecosystems amplifies digital risks.
• Astroturfing actors leverage negative emotions to create self-reinforcing contagion cycles.
• Emotion-aware risk assessment and context-sensitive moderation are crucial for platform governance.