## 7 Limitation and Future Work  
Although this study provides valuable insights into fan interactions on Weverse, particularly in relation to BTS, and has generalizability across the size and diversity of fandoms, several limitations must be acknowledged. First, our analysis focuses on a single celebrity with a large, global random. The behavioral patterns of post-heavy and comment-heavy groups could differ when examining other celebrities, smaller communities, or entirely different fan cultures. This limitation raises questions about the broader generalizability of our findings, suggesting that future work should explore more diverse populations or random contexts such as the characteristics of different groups within random, such as the gender or nationality of artists. Nevertheless, our core approach--differentiating user groups by posting and commenting activities and analyzing their linguistic behavior--could be readily applied to other fandoms. We also defined heavy users based on high-level engagement (i.e., posts and comments) rather than lower-level metrics such as views, likes, or follows [24]. Therefore, our study centers on active, public contributors and may underrepresent \"lurkers\", who read content but do not post or comment [76]. Although our approach implicitly includes some users who rarely engage (for instance, those with zero comments but at least one post), it excludes individuals who never share any content, and we do not know their exact numbers. These lurkers can still influence the community through lower-level engagement such as views, likes, and follows [77]. Future work could explore whether heavy users defined by these alternative engagement types exhibit distinct interaction patterns compared to the post-heavy and comment-heavy groups identified here. Additionally, our linguistic analysis primarily focused on English-language content because English constituted the largest portion of posts and comments on Weverse (42.4% of posts and 35.8% of comments). Although this approach yielded sufficient data for robust statistical analyses, it inevitably excludes texts in other languages that may reveal important cultural or linguistic nuances. Furthermore, Weverse is an anonymous global platform that collects no demographic information (e.g., age, gender, or nationality), and even English-language posts may be authored by non-native speakers. This introduces variability in language proficiency and style that was not explicitly addressed in our study. Future research could expand upon our findings by investigating content in additional languages, employing careful translation methods to minimize bias, or incorporating demographic factors where available. Such efforts would likely offer deeper insights into the cultural and linguistic dimensions of user engagement in global random communities. Furthermore, in using a k-means clustering approach, we assumed that heavy users would naturally separate into two main categories: post-heavy and comment-heavy. Although the results supported this assumption, k-means is known to be sensitive to outliers. We observed a small number of users who were highly active in both posts and comments; these individuals were nominally assigned to one of the heavy clusters yet represent a potentially distinct subgroup that warrants further investigation. Although our proposed design implications offer targeted strategies for improving digital random experiences, several possibilities merit further exploration. As random engagement becomes increasingly influenced by Industry 4.0 technologies, such as AI-powered content recommendations and automated moderation, it is important to understand how AI algorithmic interventions will affect user behavior in their random activities. Future studies should examine how AI interventions, such as AI-based personalization and real-time language translation, impact different engagement behaviors within digital fandoms.