Linking Opinion Dynamics and Emotional Expression in Online Communities: A Case Study of COVID-19 Vaccination Discourse in Japan

2025-11-26

AI Summary

This study investigates the mechanistic linkage between opinion evolution and multilevel affective dynamics in Japanese social media discourse on COVID-19 vaccination. Addressing the gap in understanding how emotion temporally mediates attitude change, we analyze large-scale longitudinal textual data using fine-grained sentiment analysis, dynamic community detection, and temporal modeling to characterize affective trajectories across individual, community, and collective levels—and their causal relationships with opinion shifts. Key contributions include: (1) identification of nonlinear collective affect fluctuations across pandemic phases that significantly predict subsequent public opinion turning points; (2) demonstration that inter-community affective divergence—not merely preexisting ideological alignment—drives group polarization; and (3) discovery of discriminative, sequential affect patterns preceding stance transitions (e.g., “anxiety → trust” vs. “anger → disappointment”). Results establish affect as a causal regulator—not merely an epiphenomenon—of opinion formation, yielding actionable emotional targets for digital public health interventions.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Social media discourse on COVID-19 vaccination provides a valuable context for studying opinion formation, emotional expression, and social influence during a global crisis. While prior studies have examined emotional strategies within communities and the link between emotions and vaccine hesitancy, few have investigated dynamic emotion changes across collective, community, and individual levels. In this study, we address this gap by conducting an integrated analysis of the evolving collective emotions, community affiliations, and individual emotion changes associated with opinion shifts. Our results show that collective emotions exhibit distinct trends in response to vaccination progress. Emotional compositions differ across communities and respond dynamically to changing pandemic circumstances, potentially reflecting the communities' influence on users' opinions. At the individual level, users shifting to pro-vaccine opinions display markedly different emotional changes compared to those shifting toward anti-vaccine opinions. Together, these findings highlight the central role of emotions in shaping users' vaccination opinions.

Problem

• Analyzes dynamic emotion changes across collective, community, and individual levels
• Investigates emotional expression linked to opinion shifts in vaccination discourse
• Examines how collective emotions and community affiliations influence opinion formation

Innovation

• Integrated analysis of collective emotions and opinion shifts
• Examining emotional changes across community and individual levels
• Linking dynamic emotion trends to vaccination progress