Location: PSH (Professor Stuart Hall Building) - 314,
Goldsmiths, University of London, Building 2
Campus Map Contributor: Yuri Rykov, National Research University Higher School of Economics, Russian Federation
Background:
Becoming Web 2.0 era (O'Reilly, 2005) associated with the popularization of social network sites (SNS) updates the sociological debate over the concept of online communities that exist on these platforms (Cavanagh, 2009). These online communities are used for very different purposes: to find likeminded others, for professional knowledge sharing, organizing protest events and other civil society activity, education, in public health, etc. SNS-based online communities relating to social groups from various spheres of social life probably differ from each other by participant's networking and communication behavior. The structure and users' interaction patterns within online communities vary depending on the platform technical features, temporal structure, external contexts (e.g. language), participants characteristics and group purposes (Baym, 1998; Preece, 2001; Gonzalez-Bailon, Kaltenbrunner, & Banchs, 2010). Recent research devoted to study of discussion communities in Twitter shows there is a relation between structural patterns of discussion networks and subjects of communication (Smith, Rainie, Shneiderman, & Himelboim, 2014). Therefore, the study of communities' functioning and structure in comparative perspective is of interest.
In this particular research we focus on online communities form different spheres of social life and with different purposes respectively: fan communities, professional communities, social movement communities.
Objective:
The research question is what are the differences between 'friendship' networks of these three types of online communities. The answer shed light on how purposes of online communities determine forms of connectivity and collective behavior within.
Methods: An empirical object are online groups in the most popular Russian SNS VK.com. Sample includes 55 groups (vary in size from 5,000 to 34,000 users) equally corresponding to three exploring types of communities: fan communities (e.g. musicians fans), professional communities (e.g. IT specialists, engineers) and social movement communities (e.g. urban, LGBT movements).
The data was available through API and was collected automatically by special software. Each group dataset includes: 1) complete data from group's 'wall' and discussion boards including users' activity stats; 2) the metadata of all participants (gender, age, location, etc); 3) the data on 'friend' relationships existing among community participants.
Nodes in the network are users participating in online groups. Ties are 'friend' relationships between them. To analyze data we use social network analysis methods and statistics (linear models, ANOVA).
Results: Fan networks have lower density and are less filled with ties, comparing to other groups. Fan networks have significantly more connected components and graph clusters, a higher value of Gini index for betweenness centrality distribution, indicating a greater fragmentation of fan communities compared with other. It means participants are less likely to use fan groups to networking with like-minded individuals and form a social capital.
Professional communities have the largest share of posting users and lowest Gini index for posted messages distribution that indicates more participatory behaviour of users in content creation and knowledge sharing. Despite the wide participation professional networks stay highly fragmented and clustered that is caused by the highest betweenness centralization.
Social movement networks are the most dense and the most internally connected, comparing to others, because the collective action require the cooperation between participants. Despite solidarity and cohesion these networks are the most centralized and unequal by degree centrality. Thus, online communities are used by movement activists to accumulate group-level social capital, but larger inequality emerges on the individual-level social capital.
Future Work: We are going to continue statistical analysis to obtain more results. Also we are planning to conduct a content analysis of these groups using topic modelling approach and techniques.
References:
Baym, N. K. (1998). The Emergence of On-Line Community. In S. G. Jones (Ed.), Cybersociety 2.0: Revisiting Computer-Mediated Communication and Community (pp. 35–68). Thousand Oaks, CA: SAGE.
Cavanagh, A. (2009). From Culture to Connection: Internet Community Studies. Sociology Compass, 3(1), 1–15. http://doi.org/10.1111/j.1751-9020.2008.00186.x
Gonzalez-Bailon, S., Kaltenbrunner, A., & Banchs, R. E. (2010). The structure of political discussion networks: a model for the analysis of online deliberation. Journal of Information Technology, 25(2), 230–243. http://doi.org/10.1057/jit.2010.2
O’Reilly, T. (2005, September 30). What is Web 2.0. Design patterns and business models for the next generation of software. Retrieved from http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html.
Preece, J. (2001). Sociability and usability in online communities: Determining and measuring success. Behaviour & Information Technology, 20(5), 347–356. http://doi.org/10.1080/01449290110084683
Smith, M. A., Rainie, L., Shneiderman, B., & Himelboim, I. (2014, February 20). Mapping twitter topic networks: From polarized crowds to community clusters. Pew Research Internet Project. Retrieved from http://www.pewinternet.org/2014/02/20/part-2-conversational-archetypes-six-conversation-and-group-network-structures-in-twitter/
