Preferential behavior in online groups

Abstract

Online communities in the form of message boards, listservs, and newsgroups continue to represent a considerable amount of the social activity on the Internet. Every year thousands of groups flourish while others decline into relative obscurity; likewise, millions of members join a new community every year, some of whom will come to manage or moderate the conversation while others simply sit by the sidelines and observe. These processes of group formation, growth, and dissolution are central in social science, and in an online venue they have ramifications for the design and development of community software.In this paper we explore a large corpus of thriving online communities. These groups vary widely in size, moderation and privacy, and cover an equally diverse set of subject matter. We present a broad range of descriptive statistics of these groups. Using metadata from groups, members, and individual messages, we identify users who post and are replied-to frequently by multiple group members; we classify these high-engagement users based on the longevity of their engagements. We show that users who will go on to become long-lived, highly-engaged users experience significantly better treatment than other users from the moment they join the group, well before there is an opportunity for them to develop a long-standing relationship with members of the group.We present a simple model explaining long-term heavy engagement as a combination of user-dependent and groupdependent factors. Using this model as an analytical tool, we show that properties of the user alone are sufficient to explain 95% of all memberships, but introducing a small amount of per-group information dramatically improves our ability to model users belonging to multiple groups.

Learning to predict reciprocity and triadic closure in social networks

“…Romero and Kleinberg [2010] study the problem of the triadic closure process and develop a methodology based on preferential attachment, for studying the directed triadic closure process. Backstrom et al [2008] propose a partitioning on the data that selects for active communities of engaged individuals.…”

“…Previous research on social relationships can be classified into three categories: link prediction [Liben-Nowell and Kleinberg 2007;Romero and Kleinberg 2010;Leskovec et al 2010;Backstrom and Leskovec 2011], relationship type inferring [Eagle et al 2009;Crandall et al 2010;Wang et al 2010;Tang et al 2011], and social behavior prediction [Backstrom et al 2008;Tan et al 2010;Yang et al 2010]. Backstrom and Leskovec [2011] propose an approach called supervised random walk to predict and recommend links in social networks.…”

Modelling and Analysis of User Behaviour in Online Communities

“…Straightforward measures such as number and frequency of posting are often used as an index of community health. For example, it has been shown that the activity of a group can be maintained in high levels by long term members, who help keep the group together [1,10]. On the other hand, it has been found that having lurkers in a community does not necessarily have a negative influence [12].…”

Predict Quit Rate of Group from User Behavioral and Social Information

“…However, the members in topical groups may not know each other in the real world. They gather in the same group as they have some common interest [17].In the QQ groups, a user's experience in group may be influenced by others' behavioral in the group [18]. For example, if someone sends a message in a group but no one replies him, he will leave this group probably.…”

“…However, the members in topical groups may not know each other in the real world. They gather in the same group as they have some common interest [17].…”