14.06.2024

Machine learning methods for identifying football fans

Researchers from Germany and UK develop a new method for detecting fans from sentiment in Tweets and explore their emotions-driven behavioral patterns.

Previous research exploring the role of belief dynamics for consumers in the entertainment industry has largely ignored the fact that emotional reactions are a function of the content and a consumer’s disposition towards certain protagonists.

Together with colleagues from the Universities in Reading, Dundee and London, Tim Pawlowski (Institute of Sports Science, University of Tübingen) analyze 19 m tweets in combination with in-play information for 380 football matches played in the English Premier League in order to contribute to the scientific literature and the managerial practice in three ways.

First, the researchers present a setting for testing how belief dynamics drive behavior which is characterized by several desirable features for empirical research. Second, they present an approach for detecting fans and haters of a club as well as neutrals via sentiment revealed in Tweets. Third, by looking at behavioral responses to the temporal resolution of uncertainty during a game, they offer a fine-grained empirical test for the popular uncertainty-of-outcome hypothesis in sports.

The study has recently been accepted for publication in the Journal of Economic Behavior & Organization.

Pawlowski, T., Rambaccussing, D., Ramirez, P., Reade, J. J., Rossi, G. (2024). Exploring entertainment utility from football games. Journal of Economic Behavior & Organization, 223, 185-198. doi 10.1016/j.jebo.2024.04.018