Classifying and Characterizing Fandom Activities: A Focus on Superfans' Posting and Commenting Behaviors in a Digital Fandom Community

Abstract

As digital fandom communities expand and diversify, user engagement patterns increasingly shape the social and emotional fabric of online platforms. In the era of Industry 4.0, data-driven approaches are transforming how online communities understand and optimize user engagement. In this study, we examine how different forms of activity, specifically posting and commenting, characterize fandom engagement on Weverse, a global fan community platform. By applying a clustering approach to large-scale user data, we identify distinct subsets of heavy users, separating those who focus on creating posts (post-heavy users) from those who concentrate on leaving comments (comment-heavy users). A subsequent linguistic analysis using the Linguistic Inquiry and Word Count (LIWC) tool revealed that post-heavy users typically employ a structured, goal-oriented style with collective pronouns and formal tones, whereas comment-heavy users exhibit more spontaneous, emotionally rich expressions enhanced by personalized fandom-specific slang and extensive emoji use. Building on these findings, we propose design implications such as pinning community-driven content, offering contextual translations for fandom-specific slang, and introducing reaction matrices that address the unique needs of each group. Taken together, our results unde