Title: Operational Choices in Generating Real Time Political Event Data
Abstract: Generating political event data in near real time using fully automated methods is now quite feasible, but no single approach has become dominant. This talk will survey some of the key differences that have emerged: these include maximizing news sources versus focusing on a small number of trusted sources; different approaches to dealing with multiple languages; the weaknesses in the widely-used CAMEO ontology and some alternatives; and the choice between open source and closed software platforms.
Biography
Philip Schrodt is a senior research scientist at the statistical consulting firm Parus Analytics. He received an M.A. in mathematics and a Ph.D. in political science from Indiana University in 1976, and has held permanent academic positions at Pennsylvania State University, the University of Kansas, and Northwestern University. He has also held research appointments in the United Kingdom and Norway, and has taught and done field research in the Middle East. Dr. Schrodt's major areas of research are quantitative models of political conflict and computational political methodology. His current research focuses on predicting political change using statistical and pattern recognition methods, research that has been supported by the U.S. National Science Foundation, the Defense Advanced Research Projects Agency, and the U.S. government's multi-agency Political Instability Task Force. Dr. Schrodt has published more than 90 articles in political science, is past president and a fellow of the Society for Political Methodology, and his Kansas Event Data System computer program won the “Outstanding Computer Software Award” from the American Political Science Association in 1995.
