Digging in the Digg Social News Website

The rise of social media aggregating websites provides platforms where users can actively publish, evaluate, and disseminate content in a collaborative way. In this paper, we present a large-scale empirical study about “Digg.com”, one of the biggest social media aggregating websites. Our analysis is based on crawls of 1.5 million users and 10 million published stories on Digg. We study the distinct network structure, the collaborative user characteristics, and the content dissemination process on Digg. We empirically illustrate that friendship relations are used effectively in disseminating half of the content, although there exists a high overlap between the interests of friends. A successful content dissemination process can also be performed by random users who are browsing and digging stories. Since 88% of the published content on Digg is defined as news, it is important for the content to obtain sufficient votes in a short period of time before becoming obsolete. Finally, we show that the synchronization of users' activities in time is the key to a successful content dissemination process. The dynamics between users' voting activities consequently decrease the efficiency of friendship relations during content dissemination. The results presented in this paper define basic observations and measurements to understand the underlying mechanism of disseminating content in current online social news aggregators. These findings are helpful to understand the influence of service interfaces and user behaviors on content dissemination.

[1]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[2]  Daniel W. Drezner,et al.  The power and politics of blogs , 2007 .

[3]  Michael Mitzenmacher,et al.  A Brief History of Generative Models for Power Law and Lognormal Distributions , 2004, Internet Math..

[4]  Tad Hogg,et al.  Using a model of social dynamics to predict popularity of news , 2010, WWW '10.

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

[6]  Bernardo A. Huberman,et al.  Cooperation and quality in wikipedia , 2007, WikiSym '07.

[7]  Jon M. Kleinberg,et al.  The structure of information pathways in a social communication network , 2008, KDD.

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

[9]  Jasmine Novak,et al.  Geographic routing in social networks , 2005, Proc. Natl. Acad. Sci. USA.

[10]  M. O. Lorenz,et al.  Methods of Measuring the Concentration of Wealth , 1905, Publications of the American Statistical Association.

[11]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[12]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

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

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

[15]  A. Montgomery.  Applying Quantitative Marketing Techniques to the Internet , 2000 .

[16]  Jure Leskovec,et al.  Planetary-scale views on a large instant-messaging network , 2008, WWW.

[17]  P. Van Mieghem,et al.  Influence of assortativity and degree-preserving rewiring on the spectra of networks , 2010 .

[18]  Joshua Porter.  Designing for the Social Web , 2008 .

[19]  Yong-Yeol Ahn,et al.  Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems , 2009, IEEE/ACM Transactions on Networking.

[20]  M. Consalvo,et al.  Fono, David, and Raynes-Goldie, Kate. . Hyperfriends and Beyond: Friendship and Social Norms on LiveJournal. , 2006 .

[21]  Joseph Moses Juran,et al.  Quality-control handbook , 1951 .

[22]  Lada A. Adamic,et al.  Internet: Growth dynamics of the World-Wide Web , 1999, Nature.

[23]  Krishna P. Gummadi,et al.  Growth of the flickr social network , 2008, WOSN '08.

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

[25]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[26]  Stefano Mossa,et al.  Truncation of power law behavior in "scale-free" network models due to information filtering. , 2002, Physical review letters.

[27]  Kate Raynes-Goldie.  Pulling sense out of today's informational chaos: LiveJournal as a site of knowledge creation and sharing , 2004, First Monday.

[28]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

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

[30]  S. Bornholdt,et al.  Scale-free topology of e-mail networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Christian Doerr,et al.  Lognormal distribution in the digg online social network , 2011 .

[32]  Virgílio A. F. Almeida,et al.  Characterizing user behavior in online social networks , 2009, IMC '09.

[33]  Piet Van Mieghem,et al.  Performance analysis of communications networks and systems , 2006 .

[34]  Diego Garlaschelli,et al.  Patterns of link reciprocity in directed networks. , 2004, Physical review letters.

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

[36]  Kristina Lerman,et al.  Social Networks and Social Information Filtering on Digg , 2006, ICWSM.

[37]  Daniel W. Drezner,et al.  Introduction: Blogs, politics and power: a special issue of Public Choice , 2007 .

[38]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.