Przemyslaw A. Grabowicz
Luca Maria Aiello
ABSTRACT
Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.
- L. M. Aiello, A. Barrat, R. Schifanella, C. Cattuto, B. Markines, and F. Menczer. Friendship prediction and homophily in social media. ACM Trans. Web, 6(2):9:1--9:33, 2012. Google Scholar
Digital Library
- L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD'06, page 44, New York, New York, USA, 2006. Google ScholarDigital Library
- L. Backstrom, R. Kumar, C. Marlow, J. Novak, and A. Tomkins. Preferential behavior in online groups. In International conference on Web search and web data mining - WSDM'08, pages 117--128, New York, NY, USA, 2008. Google ScholarDigital Library
- N. L. Collins and L. C. Miller. Self-disclosure and liking: A meta-analytic review. Psychological Bulletin, 166(3):457--475, 1994.Google ScholarCross Ref
- A. Cox, P. Clough, and S. Siersdorfer. Developing metrics to characterize Flickr groups. Journal of the American Society for Information Science and Technology, 62(1):493--506, 2011. Google ScholarDigital Library
- J. N. Cummings, B. Butler, and R. Kraut. The quality of online social relationships. Comm. of the ACM, 45(7):103--108, 2002. Google ScholarDigital Library
- R. I. M. Dunbar. The social brain hypothesis. Evolutionary Anthropology, 6:178--190, 1998.Google ScholarCross Ref
- S. Fortunato. Community detection in graphs. Physics Reports, 486(3--5):75--174, 2010.Google Scholar
- P. A. Gloor and Y. Zhao. Analyzing Actors and Their Discussion Topics by Semantic Social Network Analysis. In Conference on Information Visualization, IV'06, pages 130--135, Washington, DC, USA, 2006. Google ScholarDigital Library
- B. Goncalves, N. Perra, and A. Vespignani. Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number. PLoS ONE, 6(8):e22656, 2011.Google ScholarCross Ref
- P. A. Grabowicz and V. M. Eguiluz. Heterogeneity shapes groups growth in social online communities. Europhys. Lett., 97(2):28002, 2012.Google ScholarCross Ref
- P. A. Grabowicz, J. J. Ramasco, and V. M. Eguiluz. Dynamics in online social networks. arXiv:1210.0808, 2012.Google Scholar
- P. A. Grabowicz, J. J. Ramasco, E. Moro, J. M. Pujol, and V. M. Eguiluz. Social Features of Online Networks: The Strength of Intermediary Ties in Online Social Media. PLoS ONE, 7(1):e29358, 2012.Google ScholarCross Ref
- S. R. Kairam, D. J. Wang, and J. Leskovec. The life and death of online groups: predicting group growth and longevity. In 5th ACM international conference on Web search and data mining, WSDM'12, pages 673--682, New York, NY, USA, 2012. Google ScholarDigital Library
- A. Lancichinetti, S. Fortunato, and F. Radicchi. Benchmark graphs for testing community detection algorithms. Physical Review E, 78:046110, 2008.Google ScholarCross Ref
- A. Lancichinetti, F. Radicchi, J. J. Ramasco, and S. Fortunato. Finding Statistically Significant Communities in Networks. PLoS ONE, 6(4):e18961, 2011.Google ScholarCross Ref
- P. J. Ludford, D. Cosley, D. Frankowski, and L. Terveen. Think different: increasing online community participation using uniqueness and group dissimilarity. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'04, pages 631--638, New York, NY, USA, 2004. Google ScholarDigital Library
- D. W. McMillan and D. M. Chavis. Sense of community: A definition and theory. Journal of Community Psychology, 14(1):6--23, 1986.Google ScholarCross Ref
- A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In 7th ACM SIGCOMM conference on Internet measurement - IMC'07, pages 29--42, San Diego, California, USA, 2007. Google ScholarDigital Library
- R.-A. Negoescu, B. Adams, D. Phung, S. Venkatesh, and D. Gatica-Perez. Flickr hypergroups. In 17th ACM international conference on Multimedia, MM'09, pages 813--816, New York, NY, USA, 2009. Google ScholarDigital Library
- R. A. Negoescu and D. Gatica-Perez. Analyzing flickr groups. In International conference on Content-based image and video retrieval, CIVR'08, pages 417--426, New York, NY, USA, 2008. Google ScholarDigital Library
- D. A. Prentice, D. T. Miller, and J. R. Lightdale. Asymmetries in attachments to groups and to their members: Distinguishing between common-identity and common-bond groups. Personality and Social Psychology Bulletin, 20(5):484--493, 1994.Google ScholarCross Ref
- C. Prieur, N. Pissard, J. Beuscart, and D. Cardon. Thematic and social indicators for Flickr groups. In Proceedings of International Conference on Weblogs and Social Media - ICWSM'08, 2008.Google Scholar
- Y. Ren, R. Kraut, and S. Kiesler. Applying Common Identity and Bond Theory to Design of Online Communities. Organization Studies, 28(3):377--408, 2007.Google ScholarCross Ref
- S. Riger and P. J. Lavrakas. Community ties: Patterns of attachment and social interaction in urban neighborhoods. American Journal of Community Psychology, 9:55--66, 1981.Google ScholarCross Ref
- K. Sassenberg. Common bond and common identity groups on the Internet: Attachment and normative behavior in on-topic and off-topic chats. Group Dynamics Theory Research And Practice, 6(1):27--37, 2002.Google ScholarCross Ref
- H. Tajfel. Social identity and intergroup relations. Cambridge University Press, 1982.Google Scholar
- L. Tang, X. Wang, and H. Liu. Group profiling for understanding social structures. ACM Trans. Intell. Syst. Technol., 3(1):15:1--15:25, 2011. Google ScholarDigital Library
- S. Utz and K. Sassenberg. Distributive justice in common-bond and common-identity groups. Group Processes and Intergroup Relations, 5(2):151--162, 2002.Google ScholarCross Ref
- H. T. Welser, E. Gleave, D. Fisher, and M. Smith. Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure, 8(2), 2007.Google Scholar
- J. Yang and J. Leskovec. Defining and evaluating network communities based on ground-truth. In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS'12, pages 3:1--3:8, New York, NY, USA, 2012. Google ScholarDigital Library
Index Terms
- Distinguishing topical and social groups based on common identity and bond theory
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