Sanjay Ram Kairam
Dan J. Wang
Jure Leskovec
ABSTRACT
We pose a fundamental question in understanding how to identify and design successful communities: What factors predict whether a community will grow and survive in the long term? Social scientists have addressed this question extensively by analyzing offline groups which endeavor to attract new members, such as social movements, finding that new individuals are influenced strongly by their ties to members of the group. As a result, prior work on the growth of communities has treated growth primarily as a diffusion processes, leading to findings about group evolution which can be difficult to explain. The proliferation of online social networks and communities, however, has created new opportunities to study, at a large scale and with very fine resolution, the mechanisms which lead to the formation, growth, and demise of online groups.
In this paper, we analyze data from several thousand online social networks built on the Ning platform with the goal of understanding the factors contributing to the growth and longevity of groups within these networks. Specifically, we investigate the role that two types of growth (growth through diffusion and growth by other means) play during a group's formative stages from the perspectives of both the individual member and the group. Applying these insights to a population of groups of different ages and sizes, we build a model to classify groups which will grow rapidly over the short-term and long-term. Our model achieves over 79% accuracy in predicting group growth over the following two months and over 78% accuracy in predictions over the following two years. We utilize a similar approach to predict which groups will die within a year. The results of our combined analysis provide insight into how both early non-diffusion growth and a complex set of network constraints appear to contribute to the initial and continued growth and success of groups within social networks. Finally we discuss implications of this work for the design, maintenance, and analysis of online communities.
- S. Aral, L. Muchnik, and A. Sundararajan. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51):21544--21549, Dec. 2009.Google Scholar
Cross Ref
- L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '06, pages 44--54, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
- R. Burt. Structural holes versus network closure as social capital. Social capital: Theory and research, pages 31--56, 2001.Google Scholar
- B. Butler. Membership size, communication activity, and sustainability: The internal dynamics of networked social structures. Information Systems Research, 12(4):346--362, 2001. Google ScholarDigital Library
- D. Centola. The spread of behavior in an online social network experiment. Science (New York, N.Y.), 329(5996):1194--1197, Sept. 2010.Google ScholarCross Ref
- D. Centola and M. Macy. Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 113(3):702--734, Nov. 2007.Google ScholarCross Ref
- N. Ducheneaut, N. Yee, E. Nickell, and R. J. Moore. The life and death of online gaming communities: a look at guilds in world of warcraft. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '07, pages 839--848, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- R. Dunbar. Coevolution of neocortex size, group size, and language in human. Behavioral and Brain Sciences, 16(4):681--735, 1993.Google ScholarCross Ref
- R. M. Fernandez and D. McAdam. Social networks and social movements: Multiorganizational fields and recruitment to mississippi freedom summer. Sociological Forum, 3:357--382, 1988. 10.1007/BF01116431.Google ScholarCross Ref
- M. Givan and M. E. J. Newman. Community structure in social and biological networks. PNAS, 99(12):7821--7826, June 2002.Google ScholarCross Ref
- M. Gomez Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pages 1019--1028, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- M. Granovetter. The Strength of Weak Ties. The American Journal of Sociology, 78(6):1360--1380, 1973.Google ScholarCross Ref
- P. Holme. Core-periphery organization of complex networks. Physical Review E, 72(046111), 2005.Google Scholar
- Q. Jones, G. Ravid, and S. Rafaeli. Information overload and the message dynamics of online interaction spaces. Information Systems Research, 15(2):194--210, 2004. Google ScholarDigital Library
- J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Statistical properties of community structure in large social and information networks. In Proceeding of the 17th international conference on World Wide Web, WWW '08, pages 695--704, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- G. Marwell and P. Oliver. The Critical Mass in Collective Action: A Micro-Social Theory. Cambridge University Press, 1993.Google ScholarCross Ref
- D. McAdam and R. Paulsen. Specifying the relationship between social ties and activism. In D. McAdam and D. A. Snow, editors, Social Movements: Readings on Their Emergence, Mobilization, and Dynamics, pages 145--157. Roxbury Publishing Co., 2007.Google Scholar
- S. Milgram. The small-world problem. Psychology Today, pages 60--67, 1967.Google Scholar
- A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, IMC '07, pages 29--42, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- E. D. Mynatt, A. Adler, M. Ito, and V. L. O'Day. Design for network communities. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '97, pages 210--217, New York, NY, USA, 1997. ACM. Google ScholarDigital Library
- M. E. J. Newman. Detecting community structure in networks. Eur. Phys. J. B, 38:321--330, 2004.Google ScholarCross Ref
- J. E. Oliver. The Strength of Weak Ties. The American Political Science Review, 94(2):361--373, 2000.Google ScholarCross Ref
- D. Prentice, D. Miller, and J. 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
- Y. Ren, R. Kraut, and S. Kiesler. Applying Common Identity and Bond Theory to Design of Online Communities. Organization Studies, 28(3):377--408, Mar. 2007.Google ScholarCross Ref
- C. T. Reviews. Making Democracy Work: Civic Traditions in Modern Italy. Cram 101. Academic Internet Publishers Incorporated, 2007.Google Scholar
- E. M. Rogers. Diffusion of Innovations, 5th Edition. Free Press, 5th edition, 2003.Google Scholar
- F. Salem and R. Mourtada. Civil movements: The impact of facebook and twitter. The Arab Social Media Report, 1(2), 2011.Google Scholar
- D. A. Snow, L. A. Zurcher, and S. Ekland-Olson. Social networks and social movements: A microstructural approach to differential recruitment. In D. McAdam and D. A. Snow, editors, Social Movements: Readings on Their Emergence, Mobilization, and Dynamics, pages 122--131. Roxbury Publishing Co., 2007.Google Scholar
- R. Stark and W. S. Bainbridge. Networks of faith: Interpersonal bonds and recruitment to cults and sects. American Journal of Sociology, 85:1376--1395, 1980.Google ScholarCross Ref
- J. Waddington and C. Whitston. Why do people join unions in a period of membership decline? British Journal of Industrial Relations, 35(4):515--546, 1997.Google ScholarCross Ref
- E. Zheleva, H. Sharara, and L. Getoor. Co-evolution of social and affiliation networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 1007--1016, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
Index Terms
- The life and death of online groups: predicting group growth and longevity
Recommendations
Predicting Member Productivity and Withdrawal from Pre-Joining Attachments in Online Production Groups
CSCW '17: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social ComputingProductive and dedicated members are critical to the success of online production communities like Wikipedia. Many communities organize in subgroups where members voluntarily work on projects of shared interest. In this paper, we investigate how members'...
The Lifecycle and Cascade of WeChat Social Messaging Groups
WWW '16: Proceedings of the 25th International Conference on World Wide WebSocial instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life they catalyze the formation of social groups, and they bring people stronger sense of community and ...
Personal networks as a case for online communities: two case studies
The paper will compare the results from two empirical studies which both deal with the idea of personal social networks and the way they evolve into community ties, with the purpose of identifying the means by which such communities can shift from real-life ...
Comments