Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-23T21:45:09.989Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election

Published online by Cambridge University Press:  04 January 2017

Zack W. Almquist  and
Carter T. Butts
Zack W. Almquist*
Affiliation:
Department of Sociology, School of Statistics, and the Minnesota Population Center, University of Minnesota, Minneapolis, MN 55455
Carter T. Butts
Affiliation:
Departments of Sociology and Statistics, Institute for Mathematical Behavioral Sciences, University of California, Irvine, CA 92697 e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention—designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs.

Type
Research Article
Information
Political Analysis , Volume 21 , Issue 4 , Autumn 2013 , pp. 430 - 448
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: The authors would like to thank the participants and organizers in the QMSS 2 Conference on Power, Decision Making, and Social Networks, University College, Dublin, Ireland; and the participants and organizers of the 4th Annual Political Networks Conference, University of Michigan (where this work won a best methodology poster award). The authors would also like to thank the anonymous reviewers for their kind and helpful suggestions. Finally, the authors would like to mention that the data and code are available at Almquist and Butts (2013a).

References

Adamic, Lada A., and Glance, Natalie. 2005. The political blogosphere and the 2004 U.S. election: Divided they blog. Proceedings of the 3rd international workshop on link discovery. LinkKDD '05 New York, USA: ACM, 36–43.Google Scholar
Almquist, Zack W., and Butts, Carter T. 2013a. Replication data for: Dynamic Network Logistic Regression: A logistic choice analysis of inter- and intra-group blog citation dynamics in the 2004 US presidential election. http://hdl.handle.net/1902.1/21916 IQSS Dataverse Network [Distributor] V1 [Version] (accessed September 2012).Google Scholar
Almquist, Zack W., and Butts, Carter T. Forthcoming. Logistic Network Regression for scalable analysis of networks with joint edge/vertex dynamics. Sociological Methodology.Google Scholar
Ammori, Marvin. 2005. A shadow government: Private regulation, free speech, and lessons from the Sinclair blogstorm. http://www.mttlr.org/voltwelve/ammori.pdf.CrossRefGoogle Scholar
Anderson, Brigham S., Butts, Carter T., and Carley, Kathleen. 1999. The interaction of size and density with graph-level indices. Social Networks 21: 239–67.Google Scholar
Butts, Carter T. 2008a. Social network analysis: A methodological introduction. Asian Journal of Social Psychology 11: 13–41.CrossRefGoogle Scholar
Butts, Carter T. 2008b. Social Network Analysis with SNA. Journal of Statistical Software 24: 1–51.CrossRefGoogle Scholar
Butts, Carter T., and Remy Cross, B. 2009. Change and external events in computer-mediated citation networks: English language weblogs and the 2004 U.S. electoral cycle. Journal of Social Structure 10: 1–29.Google Scholar
Cartwright, Dorwin, and Harary, Frank. 1956. Structural balance: A generalization of Heider's theory. Psychological Review 63: 277–93.Google Scholar
Cone, Edward. 2003. The marketing of the president 2004. Baseline Magazine.Google Scholar
Cranmer, Skyler J., and Desmarais, Bruce A. 2011. Inferential network analysis with exponential random graph models. Political Analysis 19: 66–86.Google Scholar
Desmarais, B. A., and Cranmer, S. J. 2012. Statistical mechanics of networks: Estimation and uncertainty. Physica A: Statistical Mechanics and Its Applications 391: 1865–76.CrossRefGoogle Scholar
Desmarais, Bruce A., and Cranmer, Skyler J. 2011. Forecasting the locational dynamics of transnational terrorism: A network analytic approach. Proceedings of the European Intelligence and Security Informatics Conference (EISIC). IEEE Computer Society.Google Scholar
Drezner, Daniel W., and Farrell, Henry. 2008. Blogs, politics, and power: A special issue of Public Choice. Public Choice 134: 1–13.CrossRefGoogle Scholar
Elder, Glen H. 1974. Children of the Great Depression: Social change in life experience. Chicago: University of Chicago Press.Google Scholar
Eppstein, David, Löffler, Maarten, and Strash, Darren. 2010. Listing all maximal cliques in sparse graphs in near-optimal time. In Algorithms and computation, eds. Cheong, Otfried, Chwa, Kyung-Yong, and Park, Kunsoo. Vol. 6506 of Lecture Notes in Computer Science, 403–14. Berlin, Heidelberg: Springer.Google Scholar
Franzese, Robert J. Jr, Hays, Jude C., and Kachi, Aya. 2012. Modeling history dependence in network-behavior coevolution. Political Analysis 20: 175–90.CrossRefGoogle Scholar
Geyer, Charles J. 2009. Trust: Trust region optimization. R package version 0.1–2. http://www.stat.umn.edu/geyer/trust/ (accessed September 2012).Google Scholar
Goffman, Erving. 1959. Presentation of self in everyday life. Garden City, NY: Doubleday Anchor Books.Google Scholar
Goodreau, Stephen M., Kitts, James A., and Morris, Martina. 2009. Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography 46: 103–25.Google Scholar
Goodreau, Steven M., Handcock, Mark S., Hunter, David R., Butts, Carter T., and Morris, Martina. 2008. A statnet tutorial. Journal of Statistical Software 24: 1–26.Google Scholar
Handcock, Mark S., Hunter, David R., Butts, Carter T., Goodreau, Steven M., and Morris, Martina. 2008. statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software 24: 1–11.Google Scholar
Hanneke, Steve, Fu, Wenjie, and Xing, Eric P. 2010. Discrete temporal models of social networks. Electronic Journal of Statistics 4: 585–605.CrossRefGoogle Scholar
Hargittai, Eszter, Gallo, Jason, and Kane, Mathew. 2008. Cross-ideological discussions among conservative and liberal bloggers. Public Choice 134: 67–86.CrossRefGoogle Scholar
Heider, Fritz. 1958. The psychology of interpersonal relations. Hillsdale, London: Lawrence Erlbaum Associates, Publishers.Google Scholar
Holland, Paul W., and Leinhardt, Samuel. 1981a. An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association 76: 33–50.Google Scholar
Holland, Paul W., and Leinhardt, Samuel. 1981b. An exponential family of probability distributions for directed graphs: Rejoinder. Journal of the American Statistical Association 76: 62–65.Google Scholar
Howard, Philip N. 2005. Deep democracy, thin citizenship: The impact of digital media in political campaign strategy. Annals of the America Academy of Political and Social Sciences 597: 153–70.Google Scholar
Hunter, David R., Goodreau, Steven M., and Handcock, Mark S. 2008. Goodness of fit of social network models. Journal of the American Statistical Association 103: 248–58.Google Scholar
Kerbel, Mathew R., and Bloom, Joel David. 2005. Blog for America and civic involvement. Harvard International Journal of Press/Politics 10: 3–27.Google Scholar
Krackhardt, David. 1987a. Cognitive social structures. Social Networks 9: 109–34.CrossRefGoogle Scholar
Krackhardt, David. 1987b. QAP partialling as a test of spuriousness. Social Networks 9: 171–86.Google Scholar
Krackhardt, David. 1988. Predicting with networks: Nonparametric multiple regression analyses of dyadic data. Social Networks 10: 359–82.Google Scholar
Krackhardt, David. 1994. Graph theoretical dimensions of informal organizations. In Computational organization theory, eds. Carley, Kathleen M. and Prietula, Michael J., 89–111. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Mayhew, Bruce H. 1980. Structuralism versus individualism: Part I, shadowboxing in the dark. Social Forces 59: 335–75.Google Scholar
McFadden, Daniel. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, ed. Zarembka, Paul, 105–42. New York: Academic Press.Google Scholar
McFadden, Daniel. 1976. The Mathematical Theory of demand models. In Behavioral travel-demand models, eds. Stopher, Peter R. and Meyburg, Arnim H. Lexington, MA: Lexington Books.Google Scholar
McPherson, Miller, Smith-Lovin, Lynn, and Cook, James M. 2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology 27: 415–44.Google Scholar
Merton, Robert K. 1968. The Matthew effect in science. Science 159: 56–63.CrossRefGoogle Scholar
Morris, Martina. 1991. A log-linear modeling framework for selective mixing. Mathematical Biosciences 107: 349–77.Google Scholar
R Development Core Team. 2010. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org (accessed September 2012).Google Scholar
Rainie, Lee, Cornfield, Michael, and Horrigan, John. 2005. The Internet and campaign 2004. Technical Report. The Pew Research Center for the People and the Press: Internet and American Life Project. Washington, DC.Google Scholar
Robins, Garry, and Pattison, Philippa. 2001. Random graph models for temporal processes in social networks. Journal of Mathematical Sociology 25: 5–41.Google Scholar
Robins, Garry, Pattison, Pip, Kalish, Yuval, and Lusher, Dean. 2007. An introduction to exponential random graph (p∗) models for social networks. Social Networks 29: 173–91.Google Scholar
Schwarz, Gideon E. 1978. Estimating the dimension of a model. Annals of Statistics 6: 461–4.Google Scholar
Shumway, Robert H., and Stoffer, David S. 2006. Time series analysis and its applications. 2nd ed. New York: Springer.Google Scholar
Snijders, Tom A. B. 1996. Stochastic actor-oriented models for network change. Journal of Mathematical Sociology 21: 149–72.Google Scholar
Snijders, Tom A. B. 2001. The statistical evaluation of social network dynamics. In Sociological methodology, eds. Sobel, M. E. and Becker, M. P., 361–95. Boston; London: Basil Blackwell.Google Scholar
Snijders, Tom A.B., and Van Duijn, M. A. J. 1997. Simulation for statistical inference in dynamic network models. In Simulating social phenomena, eds. Conte, R., Hegselmann, R., and Terna, P., 493–512. Berlin: Springer.Google Scholar
Snijders, Tom A.B., Pattison, Philippa E., Robins, Garry L., and Handcock, Mark S. 2006. New specifications for exponential random graph models. Sociological Methodology 36: 99–153.Google Scholar
Sorokin, Pitrim A. 1957. Social and cultural dynamics. Boston, MA: Porter Sargent.Google Scholar
Strauss, David, and Ikeda, Michael. 1990. Psuedolikelihood estimation for social networks. Journal of the American Statistical Association 85: 204–12.Google Scholar
Wall, Melissa. 2005. Blogs of war. Journalism 6: 153–72.Google Scholar
Wasserman, Stanley, and Faust, Katherine. 1994. Social network analysis: Methods and applications. New York: Cambridge University Press.Google Scholar
Wellman, Barry. 2001. Computer networks as social networks. Science 293: 2031–4.Google Scholar
Woodly, Deva. 2008. New competencies in democratic communication? Blogs, agenda setting, and political participation. Public Choice 134: 109–23.Google Scholar