Empirical Analysis of User Life Span in Microblog
The aim of this work is to study two kinds of user life spans and their connection to the distribution of followers, friends and statuses in microblog. Both the user activity spans and user age approximately follow a two-part exponential distribution. Moreover, the users’ average number of followers and statuses increases linearly with the active span, but the average number of friends does not change significantly during the life span. We plot the distribution of users, followers, friends, and statuses as a cumulative sum to obtain a strict power-law form, which indicates an allometric growth phenomenon. These new findings show that the users’ production capacity is consistent and with self-similar growth ability in different user activity spans. We argue that the scale effect of user count development is the reason for the allometric growth phenomenon in microblog.
KeywordsLife spans Power-law Allometric growth Microblog
This work has been supported by the National Natural Science Foundation of China under Grant 61172072, 61271308, the Beijing Natural Science Foundation under Grant 4112045, the Research Fund for the Doctoral Program of Higher Education of China under Grant W11C100030, the Beijing Science and Technology Program under Grant Z121100000312024.
- 1.Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, ACM, San Diego, California, USA, pp 29–42Google Scholar
- 2.Ahn Y-Y, Han S, Kwak H, Moon S, Jeong H (2007) Analysis of topological characteristics of huge online social networking services. In: Proceedings of the 16th international conference on world wide web, ACM, Banff, Alberta, Canada, pp. 835–844Google Scholar
- 3.Jiang J, Wilson C, Wang X, Huang P, Sha W, Dai Y, Zhao BY (2010) Understanding latent interactions in online social networks. In: Proceedings of the 10th annual conference on Internet measurement, ACM, Melbourne, Australia, pp 369–382Google Scholar
- 5.Kumar R, Novak J, Tomkins A (2006) Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, Philadelphia, PA, USA, pp 611–617Google Scholar
- 7.Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web, ACM, Raleigh, North Carolina, USA, pp 591–600Google Scholar
- 12.Ribeiro B, Gauvin W, Liu B, Towsley D (2010) On MySpace account spans and double Pareto-like distribution of friends. In: Proceedings of INFOCOM IEEE conference on computer communications workshops, pp 1–6Google Scholar
- 13.Huberman BA, Adamic LA (1999) Internet: growth dynamics of the world-wide web. Nature 401:131Google Scholar
- 14.Fan P, Li P, Jiang Z, Li W, Wang H (2011) Measurement and analysis of topology and information propagation on Sina-Microblog. In: Proceedings of IEEE international conference on intelligence and security informatics (ISI), Beijing, pp 396–401Google Scholar