Mining Text Networks
David
Banks

Duke University

Thursday, October 1, 2015 - 3:30pm

The last decade has seen substantial progress in topic modeling, and considerable progress in the study of dynamic networks.  This research combines these threads, so that the network structure informs topic discovery and the identified topics predict network behavior.  The data consist of text and links from all U.S. political blogs curated by Technorati during the calendar year 2012.  A particular advantage of the model used in this research is that it naturally enforces cluster structure in the topics, through a block model for the bloggers.

Biosketch:  David Banks is a professor in the Department of Statistical Science at Duke University.  He works on Bayesian game theory, networks, risk analysis, and a few other things.  He got his Ph.D. from Virginia Tech in 1984, and has worked at various places:  UC Berkeley, the University of Cambridge, Carnegie Mellon University, and NIST, the DOT, and the FDA.  He has been the coordinating editor of the Journal of the American Statistical Association and currently is the editor of Statistics and Public Policy.

https://stat.duke.edu/~banks/