Allies and Rivals: Modeling Citation Dynamics among Party-Credentialed Blogs in the 2004 US Electoral Cycle∗

The 2004 US Presidential Election cycle marked the debut of Internet-based media such as blogs and social networking websites as institutionally recognized features of the American political landscape. Particularly significant was the credentialing of selected blogs as officially designated media sources for purposes of covering the major political party conventions, an act which gave particular legitimacy to two contending groups of partisan blogs (one credentialed for the Republican National Convention (RNC) and the other for the Democratic National Convention (DNC)). In the months that followed, these blogs served as significant foci for online journalistic, promotional, fund-raising, and organizing activities relating to the 2004 election. In this study, we employ a dynamic logistic choice model to study the dynamics of interaction within and between these two groups of political blogs. Using a longitudinal sample of all DNC and RNC-designated blog citation networks (sampled at six hour intervals for approximately four months) from Butts and Cross (2009) we are able to test for 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 (i.e., hyperlink to) one another over time. Capitalizing on the temporal resolution of our data, we utilize an autoregressive network regression framework to carry out inference for a logistic choice process closely related to the actororiented framework of Snijders (2001). Using a combination of deviance-based model selection criteria (e.g. BIC) and simulation-based goodness-of-fit tests akin to Hunter et al. (2008), we identify the combination of processes that best characterizes the choice behavior of the contending blogs. We conclude with a discussion of the potential for autoregressive network regression as a practical way of “scaling up” dynamic choice models for use with high-resolution data sets.