Jump off the Bandwagon? Characterizing Bandwagon Fans' Future Loyalty in Online NBA Fan Communities
Yichen Wang, Qin Lv
TL;DR
This study investigates the long-term loyalty trajectories of bandwagon fans in online NBA communities on Reddit, introducing a three-way loyalty taxonomy (loyal, partial-loyal, unloyal) and leveraging activity, language, and ego-network analyses across four NBA seasons. It uses flair-based identification to track bandwagon behavior and shows that most bandwagon fans revert to their home-team loyalties in subsequent seasons, with varying retention by category. Loyal and partial-loyal fans display higher activity and more sport-focused language, whereas unloyal fans exhibit denser, more influential-looking networks; these features enable a random forest model to predict next-season loyalty with notable gains over baselines, especially using activity and network signals. The findings offer practical insights for community managers and advance understanding of online loyalty dynamics, suggesting bandwagoning is typically a temporary detour rather than a lasting shift.
Abstract
Online user dynamics has been actively studied in recent years and bandwagon behavior is one of the most representative topics which can provide valuable insights for user identity change. Many previous studies have characterized bandwagon users and leveraged such characteristics to tackle practical problems such as community loyalty prediction. However, very few of them have investigated bandwagon dynamics from a long-term perspective. In this work, we focus on characterizing and predicting long-term bandwagon user behaviors in the context of online fan loyalty. Using a dataset collected from NBA-related discussion forums on Reddit, we trace the long-term loyalty status of bandwagon fans to capture their latent behavioral characteristics and then propose a computational model to predict their next sport season loyalty status with their home teams. Our analyses reveal that bandwagoning for most fans is a temporary switch and most of them will be back in the long term. In addition, online fans with different loyalty levels to their home teams have demonstrated different behaviors in various aspects, such as activity level, language usage and reply network properties. We then propose a model based on such behavioral characteristics to predict their next-season loyalty status. Its promising performance demonstrates the effectiveness of our behavior characterization.