## 5 Results
### Linguistic Usage Patterns  
To address RQ3 (\"In what ways do linguistic styles differ among these activity-based user groups?\"), we used the Linguistic Inquiry and Word Count (LIWC) tool to examine the stylistic and tonal features of user-generated text. For practical and methodological reasons, we filtered and deduplicated only the English-language posts and comments (see Table 7). Although Weverse is multilingual, English is the most commonly used language on the platform (42.4% of posts and 35.8% of comments; see Table 2), functioning as a global lingua franca that can be accessed and understood by a broad international audience. We also observed that English-language posts and comments tended to have the largest influence in the community, since non-English messages can be less accessible to users who do not speak those languages. Focusing on English-language content avoids the additional complexity of translations, which may introduce unintended shifts in nuance or style. Meanwhile, carrying out linguistic analyses in each non-English language would require dictionaries or models attuned to different cultural and grammatical structures, potentially leading to incomparable results. Given these considerations, we decided to conduct our analyses on the widely used and globally accessible English texts. In total, this process yielded 59,910 posts \begin{table}
\begin{tabular}{l r r r r r} \hline \hline \multirow{2}{*}{**Clustering**} & \multirow{2}{*}{**Users**} & \multicolumn{2}{c}{**Posts**} & \multicolumn{2}{c}{**Comments**} \\ \cline{3-6}  & & **Count** & **Users** & **Count** & **Users** \\ \hline General & 3301 & 92,087 & 2858 & 219,989 & 3032 \\ Comment-heavy & 71 & 6191 & 62 & 212,356 & 71 \\ Post-heavy & 38 & 20,114 & 38 & 10,455 & 32 \\ \hline
**Total** & 3410 & 118,392 & 2958 & 442,800 & 3135 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deduplicated data. \begin{table}
\begin{tabular}{l r r r r r} \hline \hline \multirow{2}{*}{**Clustering**} & \multirow{2}{*}{**Users**} & \multicolumn{2}{c}{**Posts**} & \multicolumn{2}{c}{**Comments**} \\ \cline{3-6}  & & **Count** & **Users** & **Count** & **Users** \\ \hline General & 2727 & 42,325 & 2120 & 100,400 & 2270 \\ Comment-heavy & 71 & 3070 & 53 & 96,814 & 71 \\ Post-heavy & 37 & 14,515 & 36 & 7551 & 32 \\ \hline
**Total** & 2835 & 59,910 & 2209 & 204,765 & 2373 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: English text-filtered data. \begin{table}
\begin{tabular}{l r r r r r} \hline \hline \multirow{2}{*}{**Clustering**} & \multirow{2}{*}{**Users**} & \multicolumn{2}{c}{**Posts**} & \multicolumn{2}{c}{**Comments**} \\ \cline{3-6}  & & **Count** & **Users** & **Count** & **Users** \\ \hline General & 3301 & 113,034 & 2858 & 235,477 & 3032 \\ Comment-heavy & 71 & 8455 & 62 & 238,253 & 71 \\ Post-heavy & 38 & 45,967 & 38 & 10,707 & 32 \\ \hline
**Total** & 3410 & 167,456 & 2958 & 484,437 & 3135 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall data. from 2209 users and 204,765 comments from 2373 users; of these, 2722 were general users, 71 were comment-heavy, and 37 were post-heavy. We used only these English posts and comments for the linguistic analysis. We then applied LIWC-22 to these English-language posts and comments. Some posts and comments could not be analyzed by LIWC because they contained no text or were too short for textual analysis. For instance, some posts included only a hashtag (for example, #BTS) along with images or videos, while some comments consisted solely of emojis (for example,, which is a symbol of unity and loyalty for BTS fans). We, therefore, removed any posts and comments that were not analyzable by LIWC from the English-filtered dataset. In total, we analyzed 55,078 posts and 195,118 comments submitted by 2747 users (see Figure 4). For comparisons between posts and comments, the value for comments is presented as a ratio (%) of the LIWC value for posts, and the \(p\) (\(t\)-test) column indicates the significance level from the \(t\)-test.