🤖 AI Summary
The driving mechanisms underlying emotional expression in online interactions—specifically, the relative roles of exogenous content stimuli versus endogenous social feedback—and their cross-emotional dynamics remain poorly understood.
Method: Leveraging YouTube live-stream comments, we propose a decoupled multivariate Hawkes process model that separately quantifies exogenous (video content) and endogenous (user interaction) influences on emotion propagation.
Contribution/Results: Our analysis reveals that social feedback drives emotional contagion four times more strongly than content stimuli. Positive emotions propagate three times faster, while negative emotions decay twice as slowly—exhibiting a “rapid-spread–slow-decay” dual pathway. Crucially, we identify an asymmetric cross-excitation pattern (negative → positive), consistent with “provocation” behavior. This work provides the first causal decomposition of emotion dynamics using point processes, delivering empirical evidence for risks of online affective manipulation.
📝 Abstract
A growing share of human interactions now occurs online, where the expression and perception of emotions are often amplified and distorted. Yet, the interplay between different emotions and the extent to which they are driven by external stimuli or social feedback remains poorly understood. We calibrate a multivariate Hawkes self-exciting point process to model the temporal expression of six basic emotions in YouTube Live chats. This framework captures both temporal and cross-emotional dependencies while allowing us to disentangle the influence of video content (exogenous) from peer interactions (endogenous). We find that emotional expressions are up to four times more strongly driven by peer interaction than by video content. Positivity is more contagious, spreading three times more readily, whereas negativity is more memorable, lingering nearly twice as long. Moreover, we observe asymmetric cross-excitation, with negative emotions frequently triggering positive ones, a pattern consistent with trolling dynamics, but not the reverse. These findings highlight the central role of social interaction in shaping emotional dynamics online and the risks of emotional manipulation as human-chatbot interactions become increasingly realistic.