All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement

By Chenhao Tan, Lillian Lee. 2015

97 citations recorded by the source corpus.

Although analyzing user behavior within individual communities is an active and rich research domain, people usually interact with multiple communities both on- and off-line. How do users act in such multi-community environments? Although there are a host of intriguing aspects to this question, it has received much less attention in the research community in comparison to the intra-community case. In this paper, we examine three aspects of multi-community engagement: the sequence of communities that users post to, the language that users employ in those communities, and the feedback that users receive, using longitudinal posting behavior on Reddit as our main data source, and DBLP for auxiliary experiments. We also demonstrate the effectiveness of features drawn from these aspects in predicting users' future level of activity. One might expect that a user's trajectory mimics the "settling-down" process in real life: an initial exploration of sub-communities before settling down into a few niches. However, we find that the users in our data continually post in new communities; moreover, as time goes on, they post increasingly evenly among a more diverse set of smaller communities. Interestingly, it seems that users that eventually leave the community are "destined" to do so from the very beginning, in the sense of showing significantly different "wandering" patterns very early on in their trajectories; this finding has potentially important design implications for community maintainers. Our multi-community perspective also allows us to investigate the "situation vs. personality" debate from language usage across different communities.

Subfields: Information Systems, Statistical and Nonlinear Physics

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Citations in the corpus

Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “…sociological studies often use students who join a school in the same year to understand the effect of interventions [22, 30], and condition on the year in which people were born to understand people’s behavior, such as variations in financial decision-making [47] or opinions on issues [19, 28].”
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension (2019, methodology): “…pmi avg , 1 .. 3 , and pmi ∗ avg , 1 .. 3 , where pmi sf,i is deﬁned as C 1 ( w ) denotes the word frequency of w in external copora (we use Reddit posts (Tan and Lee, 2015)), and C 2 ( w 1 , w 2 ) represents the co-occurrence frequency of word w 1 and w 2 within a distance K in external copora.”
Community Interaction and Conflict on the Web (2018, background): “However, analyses of intercommunity interaction and conflict are largely absent in previous work on web communities, which tend to focus on community detection [18, 80], interactions within individual communities [38, 47, 49], or how users spread their time between multiple communities [26, 76, 83].”
Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits (2020, background): “This finding is also consistent with past work on Reddit communities which found that users’ language style does not carry over and is different in different communities (Tan and Lee 2015).”
Characterizing the Language of Online Communities and its Relation to Community Reception (2016, background): “In a multi-community setting, Tan and Lee (2015) found that users tend to explore more in different communities as they mature, adopting the language of these new communities.”