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A Method of Automated Nonparametric Content Analysis for Social Science


  • Replication materials are available at Hopkins and King (2009); see Our special thanks to our indefatigable undergraduate coders Sam Caporal, Katie Colton, Nicholas Hayes, Grace Kim, Matthew Knowles, Katherine McCabe, Andrew Prokop, and Keneshia Washington. Each coded numerous blogs, dealt with the unending changes we made to our coding schemes, and made many important suggestions that greatly improved our work. Matthew Knowles also helped us track down and understand the many scholarly literatures that intersected with our work, and Steven Melendez provided invaluable computer science wizardry; both are coauthors of the open source and free computer program that implements the methods described herein (ReadMe: Software for Automated Content Analysis; see We thank Ying Lu for her wisdom and advice, Stuart Shieber for introducing us to the relevant computer science literature, and for getting us started with more than a million blog URLs. Thanks to Ken Benoit, Doug Bond, Justin Grimmer, Matt Hindman, Dan Ho, Pranam Kolari, Mark Kantrowitz, Lillian Lee, Will Lowe, Andrew Martin, Burt Monroe, Stephen Purpura, Phil Schrodt, Stuart Shulman, and Kevin Quinn for helpful suggestions or data. Thanks also to the Library of Congress (PA#NDP03-1), the Center for the Study of American Politics at Yale University, the Multidisciplinary Program on Inequality and Social Policy, and the Institute for Quantitative Social Science for research support.

Daniel J. Hopkins is Assistant Professor of Government, Georgetown University, 681 Intercultural Center, Washington, DC 20057 (, Gary King is Albert J. Weatherhead III University Professor, Harvard University, Institute for Quantitative Social Science, 1737 Cambridge St., Cambridge, MA 02138 (,


The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a given category. Unfortunately, even a method with a high percent of individual documents correctly classified can be hugely biased when estimating category proportions. By directly optimizing for this social science goal, we develop a method that gives approximately unbiased estimates of category proportions even when the optimal classifier performs poorly. We illustrate with diverse data sets, including the daily expressed opinions of thousands of people about the U.S. presidency. We also make available software that implements our methods and large corpora of text for further analysis.

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