Online forums in Chinese universities play an important role in understanding collective behavior of college students. Of particular interest are community and popularity. We address these two issues by examining data from Bulletin Board Systems (BBSs) of four Chinese universities. To characterize users’ behavior, we introduce a hypothesis test to infer individual preferred boards, which yields a polarization of users. We also perform a multilevel algorithm to detect communities of each BBS network. We measure the similarity between the board-preferred polarization and the algorithmically identified community structure by quantitative and visual tools. The resulting discrepancy indicates that board labels are inadequate to represent underlying communities. To reveal online popularity, we employ latent Dirichlet allocation to mine topics from threads to compare popularity in different universities. Based on which, we implement the Cox-Stuart test to explore the change in popularity over time and reproduce significantly ascending and descending topics around a decade. Finally, we devise a two-step model based on users’ preference and interests to reproduce the observed connectivity patterns.
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