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Title: 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      
dateReleased:
10-02-2014
downloadURL: http://hdl.handle.net/1902.1/21916
ID:
hdl:1902.1/21916
description:
Methods for analysis of network dynamics have seen great progress in the past decade. This paper shows how current methods of Dynamic Network logistic- Regression (DNR; 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 de- but of Internet-based media such as blogs and social networking websites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all DNC/RNC-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.
description:
Zack W. Almquist; Carter T. Butts, 2013, "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, Harvard Dataverse, V2
name:
Zack W. Almquist
Carter T. Butts
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997