Transformer-Based Emotion and Conflict Analysis of Disaster-Related Social Media: An Actor-Aware Decision Support Framework

Abstract

Social media platforms have become critical communication environments during disasters, where individuals express emotions, share information, and engage in public discourse. These platforms also reflect heterogeneous communication patterns shaped by different actor groups. However, existing studies predominantly focus on emotion classification and often overlook the combined role of actor identity and conflict dynamics. To address this gap, this study proposes an integrated AI-based analytical framework for actor-aware emotion and conflict analysis in post-disaster social media. An expert-annotated Turkish tweet dataset was constructed based on Ekman's emotion model, including anger, fear, sadness, happiness, and surprise, along with an additional irrelevant/off-topic category and conflict-level labels. A Transformer-based model (BERTurk) was fine-tuned for multi-class emotion classification. Experimental results show that the proposed model achieves strong classification performance, with an accuracy of 0.931 and an F1-score of 0.912, outperforming conventional machine learning and deep learning baselines. Actor-based analysis reveals systematic differences in emotional and conflict patterns across groups. Scientists, journalists, and individual users exhibit higher levels of conflict and more pronounced negative emotional expressions, whereas institutionally oriented actors display comparatively balanced and supportive communication patterns. In addition, a web-based decision support system was developed to enable interactive visualization and actor-level exploration of emotional and conflict dynamics. Overall, the proposed framework provides a scalable, analytically robust approach to understanding social media discourse in disaster contexts and offers practical implications for AI-driven crisis communication and decision-support systems.