Beyond anger: uncovering complex emotional patterns between cyberbullying roles through affective computing and epistemic network analysis

Although emotions are regarded as essential in automatic cyberbullying detection, the nuanced links between emotion types and roles remain underexplored. The dynamics of cyberbullying are therefore somewhat ambiguous. To address these issues, we analyzed the emotional patterns and connections between five cyberbullying roles (bullies, outsiders, assistants, defenders, and reporters) on a Chinese social media platform. Six emotions were extracted from 11,601 comments using a large pre-trained model for affective computing. Through epistemic network analysis, this study identified three co-occurrence patterns of emotional expressions among these roles, namely, anger-dominated negative pattern, happiness-anger conflicting pattern, and surprise-fear moderate pattern. Beyond just Angry, three emotions (Fearful, Happy, and Surprised) varied significantly among nearly all roles. In addition to the valence of emotions, the position of these roles within the overall network may also be associated with different levels of emotional arousal. Results of subtracted networks for three role pairs further indicated that these emotional co-occurrences may help identify roles for their perceptions, judgments, and intentions regarding others. These insights hold promise for enhancing targeted bullying detection and intervention.