Publications by Author: Daniel Hopkins

2015
System for Estimating a Distribution of Message Content Categories in Source Data (2nd)
Gary King, Daniel Hopkins, and Ying Lu. 11/17/2015. “System for Estimating a Distribution of Message Content Categories in Source Data (2nd).” United States of America US 9,189,538 B2 (U.S Patent and Trademark Office).Abstract
A method of computerized content analysis that gives "approximately unbiased and statistically consistent estimates" of a distribution of elements of structured, unstructured, and partially structured soruce data among a set of categories. In one embodiment, this is done by analyzing a distribution of small set of individually-classified elements in a plurality of categories and then using the information determined from the analysis to extrapolate a distribution in a larger population set. This extrapolation is performed without constraining the distribution of the unlabeled elements to be euqal to the distribution of labeled elements, nor constraining a content distribution of content of elements in the labeled set (e.g., a distribution of words used by elements in the labeled set) to be equal to a content distribution of elements in the unlabeled set. Not being constrained in these ways allows the estimation techniques described herein to provide distinct advantages over conventional aggregation techniques.
Patent
2012
System for Estimating a Distribution of Message Content Categories in Source Data
Daniel Hopkins, Gary King, and Ying Lu. 2012. “System for Estimating a Distribution of Message Content Categories in Source Data.” United States of America 8,180,717 (May 15).Abstract

A method of computerized content analysis that gives “approximately unbiased and statistically consistent estimates” of a distribution of elements of structured, unstructured, and partially structured source data among a set of categories. In one embodiment, this is done by analyzing a distribution of small set of individually-classified elements in a plurality of categories and then using the information determined from the analysis to extrapolate a distribution in a larger population set. This extrapolation is performed without constraining the distribution of the unlabeled elements to be equal to the distribution of labeled elements, nor constraining a content distribution of content of elements in the labeled set (e.g., a distribution of words used by elements in the labeled set) to be equal to a content distribution of elements in the unlabeled set. Not being constrained in these ways allows the estimation techniques described herein to provide distinct advantages over conventional aggregation techniques.

Patent
2010
Improving Anchoring Vignettes: Designing Surveys to Correct Interpersonal Incomparability
Daniel Hopkins and Gary King. 2010. “Improving Anchoring Vignettes: Designing Surveys to Correct Interpersonal Incomparability.” Public Opinion Quarterly, Pp. 1-22.Abstract

We report the results of several randomized survey experiments designed to evaluate two intended improvements to anchoring vignettes, an increasingly common technique used to achieve interpersonal comparability in survey research.  This technique asks for respondent self-assessments followed by assessments of hypothetical people described in vignettes. Variation in assessments of the vignettes across respondents reveals interpersonal incomparability and allows researchers to make responses more comparable by rescaling them. Our experiments show, first, that switching the question order so that self-assessments follow the vignettes primes respondents to define the response scale in a common way.  In this case, priming is not a bias to avoid but a means of better communicating the question’s meaning.  We then demonstrate that combining vignettes and self-assessments in a single direct comparison induces inconsistent and less informative responses.  Since similar combined strategies are widely employed for related purposes, our results indicate that anchoring vignettes could reduce measurement error in many applications where they are not currently used.  Data for our experiments come from a national telephone survey and a separate on-line survey.

Article
A Method of Automated Nonparametric Content Analysis for Social Science
Daniel Hopkins and Gary King. 2010. “A Method of Automated Nonparametric Content Analysis for Social Science.” American Journal of Political Science, 54, 1, Pp. 229–247.Abstract

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.

This article led to the formation of Crimson Hexagon

Article