Change and External Events in Computer-Mediated Citation Networks: English Language Weblogs and the 2004 U.S. Electoral Cycle*

This study examines global patterns of stability and change within six longitudinal samples of English-language weblogs (or “blogs”) during the 2004 U.S. Presidential election campaign. Using distance-based methods of graph comparison, we explore the evolution of the blog-blog citation networks for each sample during the period. In addition to describing the qualitative dynamics of the blog networks, we relate major campaign events (e.g., party political conventions and debates) to the observed pace of change. As we demonstrate, such events are associated with substantial differences in overall network volatility; moreover, volatility is also shown to have strong seasonal and endogenous components. Our findings suggest that external factors (both regular and episodic) may be important drivers of network dynamics.

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

[2]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[3]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[4]  Peter R. Monge,et al.  Theories of Communication Networks , 2003 .

[5]  P. Howard Deep Democracy, Thin Citizenship: The Impact of Digital Media in Political Campaign Strategy , 2005 .

[6]  B. Wellman Computer Networks As Social Networks , 2001, Science.

[7]  Thomas W. Valente,et al.  The stability of centrality measures when networks are sampled , 2003, Soc. Networks.

[8]  Tim Weninger,et al.  Collaborative and Structural Recommendation of Friends using Weblog-based Social Network Analysis , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[9]  Pramod K. Nayar,et al.  The Internet in everyday life , 2004, J. Assoc. Inf. Sci. Technol..

[10]  Prashant Bordia,et al.  Problem Solving in Social Interactions on the Internet: Rumor As Social Cognition , 2004 .

[11]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[12]  P. Killworth,et al.  The Problem of Informant Accuracy: The Validity of Retrospective Data , 1984 .

[13]  Stefan Richter,et al.  Centrality Indices , 2004, Network Analysis.

[14]  S K Thompson,et al.  Adaptive sampling in behavioral surveys. , 1997, NIDA research monograph.

[15]  Carter T. Butts,et al.  Network inference, error, and informant (in)accuracy: a Bayesian approach , 2003, Soc. Networks.

[16]  Minas Gjoka,et al.  A Walk in Facebook: Uniform Sampling of Users in Online Social Networks , 2009, ArXiv.

[17]  P. Howard,et al.  Society Online: The Internet in Context , 2003 .

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

[19]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[20]  Kathleen M. Carley,et al.  Electronic Mail and Scientific Communication , 1991 .

[21]  Petter Holme,et al.  Structure and time evolution of an Internet dating community , 2002, Soc. Networks.

[22]  Kathleen M. Carley,et al.  Some Simple Algorithms for Structural Comparison , 2005, Comput. Math. Organ. Theory.

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

[24]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.