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Scraping Public Co-Occurrences for Statistical Network Analysis of Political Elites

  • Paasha Mahdavi


Collecting network information on political elites using conventional methods such as surveys and text records is challenging in authoritarian and/or conflict-ridden states. I introduce a data collection method for elite networks using scraping algorithms to capture public co-appearances at political and social events. Validity checks using existing data show the method effectively replicates interaction-based networks but not networks based on behavioral similarities; in both cases, measurement error remains a concern. Applying the method to Nigeria illustrates that patronage—measured in terms of public connectivity—does not drive national oil companies appointments. Given that theories of elite behavior aim to understand individual-level interactions, the applicability of data using this technique is well-suited to situations where intrusive data collection is costly or prohibitive.



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Paasha Mahdavi is an Assistant Professor in the McCourt School of Public Policy, Georgetown University, Old North, Suite 100, 3700 O Street NW, Washington, DC 20057 ( This paper has benefited from discussions with Nicholas Beauchamp, Graeme Blair, Bruce Desmarais, Mark Handcock, John Ishiyama, Franziska Keller, Jeffrey Lewis, Lauren Peritz, Brandon Stewart, Josef Woldense and participants at the 2014 meeting of the Society for Political Methodology, the 2015 meeting of the American Political Science Association, and the UCLA working group on statistical network analysis. The author thanks the editors and three anonymous reviewers for their comments and suggestions. All errors are the authors own. To view supplementary material for this article, please visit



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Scraping Public Co-Occurrences for Statistical Network Analysis of Political Elites

  • Paasha Mahdavi


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