Characterizing and Modeling the Dynamics of Activity and Popularity

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

[1]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[2]  Bruno S. Silvestre,et al.  Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media , 2011 .

[3]  Z. Di,et al.  Emergence of global preferential attachment from local interaction , 2009, 0910.0103.

[4]  Jure Leskovec,et al.  From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews , 2013, WWW.

[5]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[6]  D. Helbing,et al.  How social influence can undermine the wisdom of crowd effect , 2011, Proceedings of the National Academy of Sciences.

[7]  Santo Fortunato,et al.  Characterizing and Modeling Citation Dynamics , 2011, PloS one.

[8]  Zhi-Dan Zhao,et al.  Scaling behavior of online human activity , 2012, ArXiv.

[9]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Damon Centola,et al.  The Spread of Behavior in an Online Social Network Experiment , 2010, Science.

[11]  Taha Yasseri,et al.  Circadian Patterns of Wikipedia Editorial Activity: A Demographic Analysis , 2011, PloS one.

[12]  Matthew J. Salganik,et al.  Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market , 2006, Science.

[13]  A. Barabasi,et al.  Dynamics of information access on the web. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Santo Fortunato,et al.  Characterizing and modeling the dynamics of online popularity , 2010, Physical review letters.

[15]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[16]  Bernardo A. Huberman,et al.  Predicting the popularity of online content , 2008, Commun. ACM.

[17]  Jukka-Pekka Onnela,et al.  Spontaneous emergence of social influence in online systems , 2009, Proceedings of the National Academy of Sciences.

[18]  Derek de Solla Price,et al.  A general theory of bibliometric and other cumulative advantage processes , 1976, J. Am. Soc. Inf. Sci..

[19]  Shilpa Chakravartula,et al.  Complex Networks: Structure and Dynamics , 2014 .

[20]  Z. Neda,et al.  Measuring preferential attachment in evolving networks , 2001, cond-mat/0104131.

[21]  C. Bauckhage,et al.  Analyzing Social Bookmarking Systems : A del . icio . us Cookbook , 2008 .

[22]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[23]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .

[24]  K. Kaski,et al.  Scale-free networks generated by random walkers , 2004, cond-mat/0404088.

[25]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[26]  Krishna P. Gummadi,et al.  A measurement-driven analysis of information propagation in the flickr social network , 2009, WWW '09.

[27]  Lucas C. Parra,et al.  Origins of power-law degree distribution in the heterogeneity of human activity in social networks , 2013, Scientific Reports.

[28]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[29]  Didier Sornette,et al.  Robust dynamic classes revealed by measuring the response function of a social system , 2008, Proceedings of the National Academy of Sciences.

[30]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[31]  Qiang Yan,et al.  Impact of Bursty Human Activity Patterns on the Popularity of Online Content , 2012 .

[32]  Ying Fan,et al.  Do scientists trace hot topics? , 2013, Scientific Reports.

[33]  Beom Jun Kim,et al.  Role of activity in human dynamics , 2007, EPL (Europhysics Letters).

[34]  SzaboGabor,et al.  Predicting the popularity of online content , 2010 .

[35]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[36]  Fang Wu,et al.  Novelty and collective attention , 2007, Proceedings of the National Academy of Sciences.

[37]  Kun Li,et al.  A coevolving model based on preferential triadic closure for social media networks , 2013, Scientific Reports.