Understanding following patterns among high-performance athletes

TL;DR

This study analyzes Twitter follower networks among Olympic athletes to understand social interaction patterns and the influence of demographic and sport-related features on their connections.

Contribution

It introduces a novel approach to studying athlete interactions using social media data and quantifies the influence of various features on connection formation.

Findings

Frequent connections occur among athletes with similar features.

The network shows significant homophily based on sex, country, and sport.

Public social media data can serve as a proxy for studying complex social systems.

Abstract

Professional sports enhance interaction among athletes through training groups, sponsored events and competitions. Among these, the Olympic Games represent the largest competition with a global impact, providing the participants with a unique opportunity for interaction. We studied the following patterns among highly successful athletes to understand the structure of their interactions. We used the list of Olympic medallists in the Tokyo 2020 Games to extract their follower-followee network in Twitter, finding 7,326 connections among 964 athletes. The network displayed frequent connections to similar peers in terms of their features including sex, country and sport. We quantified the influence of these features in the followees choice through a gravity approach capturing the number of connections between homogeneous groups. Our research remarks the importance of datasets built from public exposure of professional athletes, serving as a proxy to investigate interesting aspects of many complex socio-cultural systems at different scales.