Abstract
Most successful online communities employ professionals, sometimes called “community managers”, for a variety of tasks including on boarding new participants, mediating conflict, and policing unwanted behaviour. We interpret the activity of community managers as network design: they take action oriented at shaping the network of interactions in a way conducive to their community’s goals. It follows that, if such action is successful, we should be able to detect its signature in the network itself. Growing networks where links are allocated by a preferential attachment mechanism are known to converge to networks displaying a power law degree distribution. Our main hypothesis is that managed online communities would deviate from the power law form; such deviation constitutes the signature of successful community management. Our secondary hypothesis is that said deviation happens in a predictable way, once community management practices are accounted for. We investigate the issue using empirical data on three small online communities and a computer model that simulates a widely used community management activity called on boarding. We find that the model produces in-degree distributions that systematically deviate from power law behavior for low-values of the in-degree; we then explore the implications and possible applications of the finding.
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References
Barabasi, A.L.: The origin of bursts and heavy tails in human dynamics. Nature 435(7039), 207–211 (2005)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. science 286(5439), 509–512 (1999)
Barabási, A.L., Albert, R., Jeong, H.: Mean-field theory for scale-free random networks. Physica A: Statistical Mechanics and its Applications 272(1), 173–187 (1999)
Bianconi, G., Barabási, A.L.: Competition and multiscaling in evolving networks. EPL (Europhysics Letters) 54(4), 436 (2001)
Borgatti, S.P., Mehra, A., Brass, D.J., Labianca, G.: Network analysis in the social sciences. science 323(5916), 892–895 (2009)
Burt, R.S.: Structural holes: The social structure of competition. Harvard university press (2009)
Clauset, A., Shalizi, C.R., Newman, M.E.: Power-law distributions in empirical data. SIAM review 51(4), 661–703 (2009)
De Liddo, A., Sándor, Á., Shum, S.B.: Contested collective intelligence: Rationale, technologies, and a human-machine annotation study. Computer Supported CooperativeWork (CSCW) 21(4-5), 417–448 (2012)
Diplaris, S., Sonnenbichler, A., Kaczanowski, T., Mylonas, P., Scherp, A., Janik, M., Papadopoulos, S., Ovelgoenne, M., Kompatsiaris, Y.: Emerging, collective intelligence for personal, organisational and social use. In: Next generation data technologies for collective computational intelligence, pp. 527–573. Springer (2011)
Dorogovtsev, S.N., Mendes, J.F.: Evolution of networks. Advances in physics 51(4), 1079–1187 (2002)
Hodas, N.O., Lerman, K.: The simple rules of social contagion. Scientific reports 4 (2014)
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pp. 56–65. ACM (2007)
Kunegis, J., Blattner, M., Moser, C.: Preferential attachment in online networks: measurement and explanations. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 205–214. ACM (2013)
Laniado, D., Tasso, R., Volkovich, Y., Kaltenbrunner, A.: When the wikipedians talk: Network and tree structure of wikipedia discussion pages. In: ICWSM (2011)
Levy, P.: Collective intelligence: Mankinds emerging world in cyberspace. Cambridge, Mass.: Perseus Books (1997)
Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., Christakis, N.: Tastes, ties, and time: A new social network dataset using facebook. com. Social networks 30(4), 330–342 (2008)
Nick, B.: Toward a better understanding of evolving social networks. Ph.D. thesis (2013)
Rheingold, H.: The virtual community: Homesteading on the electronic frontier. MIT press (1993)
Shirky, C.: Here comes everybody: The power of organizing without organizations. Penguin (2008)
Shum, S.B.: The roots of computer supported argument visualization. In: Visualizing argumentation, pp. 3–24. Springer (2003)
Slegg, J.: Facebook news feed algorithm change reduces visibility of page updates (2014). URL http://searchenginewatch.com/sew/news/2324814/facebook-news-feed-algorithm-tweak-reduces-visibility-of-page-updates
Zanetti, M.S., Sarigol, E., Scholtes, I., Tessone, C.J., Schweitzer, F.: A quantitative study of social organisation in open source software communities. arXiv preprint arXiv:1208.4289 (2012)
Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th international conference on World Wide Web, pp. 221–230. ACM (2007)
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Cottica, A., Melançon, G., Renoust, B. (2017). Testing for the signature of policy in online communities. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_4
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DOI: https://doi.org/10.1007/978-3-319-50901-3_4
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