Abstract
The study of user diversity in online social networks is an important and ongoing research effort to better understand human behavior. This work takes a step in this direction by providing an empirical study of around 8,000 athletes divided into 13 categories and followed by 197 million users in Twitter. We propose a metric for follower diversity at the category level that factors the vast popularity difference between categories (e.g., soccer versus golf). Using this metric, we propose a measure for athlete heterogeneity based on the diversity of his/her followers. Our findings reveal that follower diversity is spread across two scales with the vast majority of users having very small diversity. We also find that athlete heterogeneity is inversely proportional to its number of followers. This indicates that very popular athletes are followed by users that (on average) do not follow other sports.
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Notes
- 1.
Note that there is a user following all athletes—most likely an account not associated with a real person.
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Acknowledgments
This work has been partially funded through research grants from the following Brazilian agencies: CNPq, CAPES and FAPERJ.
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© 2016 Springer International Publishing Switzerland
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Silveira, R., Iacobelli, G., Figueiredo, D. (2016). An Empirical Study of the Diversity of Athletes’ Followers on Twitter. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_25
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DOI: https://doi.org/10.1007/978-3-319-30569-1_25
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