Temporal dynamics of emotions in Italian online soccer fandoms

This study investigates the emotional dynamics of Italian soccer fandoms through computational analysis of user-generated content from official Instagram accounts of 83 teams across Serie A, Serie B, and Lega Pro during the 2023-24 season. By applying sentiment analysis to fan comments, we extract temporal emotional patterns and identify distinct clusters of fan bases with similar preseason expectations. Drawing from complex systems theory, we characterize joy as displaying anti-bursty temporal distributions, while anger is marked by pronounced bursty patterns. Our analysis reveals significant correlations between these emotional signals, preseason expectations, socioeconomic factors, and final league rankings. In particular, the burstiness metric emerges as a meaningful correlate of team performance, as supported by stepwise regression. These findings offer novel insights into the relationship between fan emotional expression and team outcomes, suggesting potential avenues for research in sports analytics, social media dynamics, and fan engagement studies.