Temporal Dynamics of Emotions in Italian Online Soccer Fandoms

2025-06-13

AI Summary

This study investigates how Italian football fans’ emotional dynamics on Instagram are shaped by preseason expectations, socioeconomic factors, and final league standings. Method: Leveraging comment data from 83 teams during the 2023–24 season, we conduct fine-grained sentiment analysis using a pretrained language model, complemented by burstiness quantification, time-series modeling, and multivariate regression. Contribution/Results: We uncover two novel findings: (i) joy exhibits anti-bursty behavior, whereas anger is highly bursty; (ii) emotional burstiness emerges as a statistically significant predictor of final league ranking (p < 0.001), accounting for 32% of model explanatory power—removal reduces R² by 32%. We further identify fan clusters with homogeneous preseason expectations and develop a high-explanatory statistical model (R² improved by 32%), offering a reproducible methodological framework for sports sociology and fan behavioral modeling.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Computational social science
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

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; statistical models excluding this parameter show a decrease in the coefficient of determination of 32%. 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.

Problem

• Analyze emotional dynamics in Italian soccer fandoms via Instagram comments
• Identify correlations between fan emotions, expectations, and team performance
• Explore burstiness patterns in joy and anger related to league rankings

Innovation

• Sentiment analysis of Instagram fan comments
• Anti-bursty joy and bursty anger patterns
• Burstiness metric correlates with team performance