## 1 Introduction  
Digital random has grown alongside the rise of online communities, fueled by technologies that enable people with shared interests to connect more easily. Fans now gather on a diverse array of digital platforms, including dedicated forums such as Reddit [1], Twitch [2], Discord [3], and Tiktok [4] as well as typical social media groups on platforms like X and Instagram [5]. Inevitably, fans' participation in these environments spans a range of media-related behaviors--views, likes, shares, posts, comments, chatting, streaming, and more--that reflect their levels of engagement.  
Within these communities, users can be classified by how deeply they involve themselves in fan activities. Individuals who display exceptional commitment are often referred to as _superfans_. Superfans regularly stand out through the quantity and quality of their contributions, going beyond ordinary participation to enrich the fan ecosystem. They play pivotal roles in online interactions (e.g., discussions, idea-sharing) and offline events (e.g., community meetups), thereby strengthening connections among long-time members while also drawing in newcomers [6]. Research has extensively examined the impact of fans on community growth and sustainability [7; 8; 9], underscoring the sociocultural significance of random and the potential for fans to collaborate in driving entertainment industry trends.  
Despite the recognized importance of superfans, most studies treat them as a homogeneous group that simply exhibits \"excessive engagement\" relative to casual fans, leaving a gap in understanding the nuances of superfan behavior. Existing scholarship has tended to focus on high-level metrics (e.g., hours spent, total posts made), without closely examining the varied types of activities or the linguistic and social characteristics that may distinguish different superfan subgroups. This study addresses the following research questions:  
1. **RQ1:** How can superfans be classified into distinct subgroups based on their activity patterns?
2. **RQ2:** How do digital random activities vary across different user groups?
3. **RQ3:** In what ways do linguistic styles differ among these activity-based user groups?  
In order to unpack these research questions, we investigate large-scale, digitally captured random activities to clarify the behavioral and linguistic traits of superfans in media-rich online environments in this study. By comparing superfans with regular fans, we propose that superfans are not defined solely by the intensity of their participation but also by the distinct types of engagement they pursue with random activities. We focus on the BTS random, also known as ARMY, one of the most popular K-pop groups with particularly active digital random, as an illustrative case recognized for its extensive and globally connected online presence. Using a dataset of 167,456 posts and 484,437 comments from 3410 users on Weverse ([[https://weverse.io/](https://weverse.io/)]([https://weverse.io/](https://weverse.io/)), accessed on 27 February 2025), a dedicated K-pop fan platform, we offer an empirical, data-driven view of how superfans operate within digital fandoms.  
Our approach involves identifying patterns of user behavior and language use to better understand the different types of superfans within digital random communities. This perspective allows us to provide a more detailed view of how superfans participate and communicate within these platforms. Based on these insights, we propose design strategies for HCI practitioners, social computing researchers, and platform developers who aim to foster more inclusive, engaging, and emotionally resonant fan experiences. By examining superfan activities in the context of broader community trends, this study highlights the potential of data-informed, user-centered design to enhance participation and belonging in digital random ecosystems.