EMOTIONAL CONTAGION MODELLING IN PROTEST MOVEMENTS USING MIXED METHODS

Abstract

Recognizing the factors that contribute to the sentiments within protest movements is imperative for understanding social dynamics and predicting changes in civic engagement. This research develops a mixed-methods framework for the analysis of emotional contagion in relation to protestors, integrating qualitative sentiment narratives with computational analysis. Using social media, protest diaries, and field interviews, we investigate how emotions such as anger, hope, and fear spread in social and physical spaces. We employ agent-based modeling and thematic content analysis to explore the interaction between individual emotions and collective behavior. Key contagion pathways, emotional triggers, and feedback loops are identified that illustrate the impact of strong sentiments on the escalation or de-escalation of protests. In addition, the impact of key stakeholders and media on the framing of narratives that shape emotions is examined. Results emphasize the necessity of affective synchrony for the sustainability of protests and illustrate the impact of emotional alignment, whether spontaneous or coordinated, on mobilization outcomes. By integrating narrative analysis and model-based computation, this study addresses the behavior of protesters, thereby contributing to the expanding domain of social computational science.