Identifying Active Subgroups In Online Communities (original) (raw)
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Using Social Network Analysis to Detect Cohesive Subgroups in Usenet Newsgroups
Two complementary Social Network Analyses were performed on large samples of messages from five Usenet newsgroups for the purpose of detecting stable cohesive subgroups of participants indicative of virtual communities. As an initial approximation to the study of potential “virtual” Communities of Practice, the research deliberately targeted five practitioner-oriented newsgroups. The first analysis searched for stable cliques and 2-plexes, and found them in four newsgroups. The second analysis fitted Borgatti-Everett’s continuous core-periphery model to the observed interaction data. It proved a good fit, also in four newsgroups. A proposed category sociomatrix and structural map of each newsgroup yielded considerable insight about various categories of participants and their interaction dynamics, helping to identify stable virtual communities in every newsgroup analysed. Social Network Analysis is thus evaluated as an effective method to detect virtual communities in Usenet newsgroups, measure the strength of their interactions, and reveal key members.
Detecting Communities and Surveying the Most Influence of Online Users
Social network is a virtual environment that provides services for connecting users with the same interests, points of view, gender, space and time. Beside connection, information exchange, communication, entertainment and so on. Social network is also an environment for users who work in online business, advertisement or politics, criminal investigation. How to know what users discuss topics via exchanged contents and communities which users join in? In this paper, we propose a model by using topic model combined with K-means to detect communities of online users. Each user in social network is represented by a vector in which the components are the distribution probabilities of interested topics of that user. Based on the components of this vector, we discover the interested topics of online users to detect communities and survey users who are the most influence in communities to recommend for spreading information on social network.
ACM SIGWEB Newsletter, 2009
Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. This thesis addresses the problem of finding cohesive subgroups within social networks inferred from online interactions. The dissertation begins with a review of relevant literature and identifies existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing the cohesive subgroups over time (Choose). Social network analysis, clustering and partitioning, and similarity measurement are then used to implement each of the steps. Two further case studies are presented, one involving the TorCamp Google group and the other involving YouTube vaccination videos, to demonstrate how the methodology works in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach can find meaningful subgroups. Additional empirical findings are reported. Betweenness centrality appears to be a useful filter for screening potential subgroup members, and members of cohesive subgroups have stronger community membership and influence than others. Subgroups identified using weighted average hierarchical clustering are con-I cannot believe that it has been almost a five and a half years journey to getting my PhD. It is true what they say, that during the PhD, you not just learn about how vi to do research, but also you learn about life. My PhD at the University of Toronto in the Interactive Media Lab has been a tremendous experience, which I will take with me in my research career. It has provided me with the foundation and tools to be a great researcher and to contribute to advancing the intellectual knowledge in this world. vii
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Online communities, or groups, have largely been defined based on links, page rank, and eigenvalues. In this paper we explore identifying abstract groups, groups where member’s interests and online footprints are similar but they are not necessarily connected to one another explicitly. We use a combination of structural information and content information from posts and their comments to build a footprint for groups. We find that these variables do a good job at identifying groups, placing members within a group, and help determine the appropriate granularity for group boundaries.
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In this paper, we are interested in answering the following research question: “Is it possible to form effective groups in virtual communities by exploiting trust information without significant overhead, similarly to real user communities?” In order to answer this question, instead of adopting the largely used approach of exploiting the opinions provided by all the users of the community (called global reputation), we propose to use a particular form of reputation, called local reputation. We also propose an algorithm for group formation able to implement the proposed procedure to form effective groups in virtual communities. Another interesting question is how to measure the effectiveness of groups in virtual communities. To this aim we introduce the Gk index in a measure of the effectiveness of the group formation. We tested our algorithm by realizing some experimental trials on real data from the real world EPINIONS and CIAO communities, showing the significant advantages of our procedure w.r.t. another prominent approach based on traditional global reputation.
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Abstract In most online social networks, with the increasing number of users and content, the problem of contact filtering becomes more and more present. The recent introduction of such features in online social networks--for instance, Circles in Google+ or Facebook Smart lists--shows that it is a problem they are confronted to. In this paper, we explore this question through multidisciplinary aspects. First, we discuss about this issue of groups management in the context of social networks.
Characterization of online groups along space, time, and social dimensions
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Social groups play a crucial role in online social media because they form the basis for user participation and engagement. Although widely studied in their static and evolutionary aspects, no much attention has been devoted to the exploration of the nature of groups. In fact, groups can originate from different aggregation processes that may be determined by several orthogonal factors. A key question in this scenario is whether it is possible to identify the different types of groups that emerge spontaneously in online social media and how they differ. We propose a general framework for the characterization of groups along the geographical, temporal, and socio-topical dimensions and we apply it on a very large dataset from Flickr. In particular, we define a new metric to account for geographic dispersion, we use a clustering approach on activity traces to extract classes of different temporal footprints, and we transpose the "common identity and common bond" theory into metrics to identify the skew of a group towards sociality or topicality. We directly validate the predictions of the sociological theory showing that the metrics are able to forecast with high accuracy the group type when compared to a human-generated ground truth. Last, we frame our contribution into a wider context by putting in relation different types of groups with communities detected algorithmically on the social graph and by showing the effect that the group type might have on processes of information diffusion. Results support the intuition that a more nuanced description of groups could improve not only the understanding of the activity of the user base but also the interpretation of other phenomena occurring on social graphs.