Being Rational or Aggressive? A Revisit to Dunbar's Number in Online Social Networks

Recent years have witnessed the explosive growth of online social networks (OSNs). They provide powerful IT-innovations for online social activities such as organizing contacts, publishing content, and sharing interests between friends who may never meet before. As more and more people become active users of OSNs, one may ponder questions such as (1) Do OSNs indeed improve our sociability? (2) To what extent can we expand our offline social spectrum in OSNs? (3) Can we identify some interesting user behaviors in OSNs? Our work in this paper attempts to answer these interesting questions. First, we systematically validate the existence of a new Dunbar@?s number in OSNs, which is ranging from 200 to 300 empirically. To reach this, we conduct local-structure analysis as well as user-interaction analysis on extensive real-world OSNs. Second, based on this new number, we divide OSN users into two categories: the rational and the aggressive, and find that rational users intend to develop close and reciprocated relationship, whereas aggressive users have no consistent behaviors. Third, we propose a simple model to highlight the constraints of time and cognition that may affect the evolution of OSNs heavily. Finally, we discuss the potential use of our findings for viral marketing and privacy management in OSNs.

[1]  Kevin Fiedler,et al.  Grooming Gossip And The Evolution Of Language , 2016 .

[2]  Seungyeop Han,et al.  Analysis of topological characteristics of huge online social networking services , 2007, WWW '07.

[3]  Anthony Bonato,et al.  A Dynamic Model for On-Line Social Networks , 2009, WAW.

[4]  Alessandro Acquisti,et al.  Information revelation and privacy in online social networks , 2005, WPES '05.

[5]  Junjie Wu,et al.  Weak ties: subtle role of information diffusion in online social networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Maksim Kitsak,et al.  Identifying influential spreaders in complex networks , 2010 .

[7]  Chi-Jie Lu,et al.  Sales forecasting of computer products based on variable selection scheme and support vector regression , 2014, Neurocomputing.

[8]  Stefan Bornholdt,et al.  Emergence of a small world from local interactions: modeling acquaintance networks. , 2002, Physical review letters.

[9]  Munmun De Choudhury,et al.  Inferring relevant social networks from interpersonal communication , 2010, WWW '10.

[10]  Ling Shao,et al.  Learning from social media network , 2012, Neurocomputing.

[11]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[12]  Jiajie Xu,et al.  Discovering hot topics from geo-tagged video , 2013, Neurocomputing.

[13]  Matthias Rauterberg,et al.  Design of social agents , 2013, Neurocomputing.

[14]  Bernardo A. Huberman,et al.  Rhythms of social interaction: messaging within a massive online network , 2006, ArXiv.

[15]  Xueming Qian,et al.  Mining user-contributed photos for personalized product recommendation , 2014, Neurocomputing.

[16]  Junjie Wu,et al.  K-core-preferred Attack to the Internet: Is It More Malicious Than Degree Attack? , 2013, WAIM.

[17]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[18]  Hamed Haddadi,et al.  To Add or Not to Add: Privacy and Social Honeypots , 2010, 2010 IEEE International Conference on Communications Workshops.

[19]  Chen-Nee Chuah,et al.  Unveiling facebook: a measurement study of social network based applications , 2008, IMC '08.

[20]  Beom Jun Kim,et al.  Growing scale-free networks with tunable clustering. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[22]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[23]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[24]  Kan Li,et al.  A unified community detection algorithm in complex network , 2014, Neurocomputing.

[25]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[26]  A. Vázquez Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[28]  M Girvan,et al.  Structure of growing social networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Thomas V. Pollet,et al.  Use of Social Network Sites and Instant Messaging Does Not Lead to Increased Offline Social Network Size, or to Emotionally Closer Relationships with Offline Network Members , 2011, Cyberpsychology Behav. Soc. Netw..

[31]  Yoshi Fujiwara,et al.  A Gap in the Community-Size Distribution of a Large-Scale Social Networking Site , 2007, ArXiv.

[32]  Mason A. Porter,et al.  Community Structure in Online Collegiate Social Networks , 2008 .

[33]  Va Alexandria,et al.  Gross, Ralph, and Acquisti, Alessandro. . Information Revelation and Privacy in Online Social Networks. , 2005 .

[34]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[35]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

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