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

Chenhao Tan, Lillian Lee
In Proceedings of the 24th International World Wide Web Conference (WWW'2015).

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.

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     author = {Chenhao Tan and Lillian Lee},
     title = {All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement},
     year = {2015},
     booktitle = {Proceedings of WWW}


This work was supported in part by NSF grant IIS-0910664 and a Google Research Grant. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other sponsors.