Integrating topic modeling and word embedding to characterize violent deaths

Alina Arseniev-Koehler, Susan D. Cochran, Vickie M. Mays, Kai-Wei Chang and Jacob G. Foster
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Alina Arseniev-Koehler: a Department of Sociology, University of California, Los Angeles, CA 90095;; b Bridging Research Innovation, Training and Education for Science, Research & Policy Center, University of California, Los Angeles, CA 90095;
Susan D. Cochran: b Bridging Research Innovation, Training and Education for Science, Research & Policy Center, University of California, Los Angeles, CA 90095;; c Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095;; d Department of Statistics, University of California, Los Angeles, CA 90095;
Vickie M. Mays: b Bridging Research Innovation, Training and Education for Science, Research & Policy Center, University of California, Los Angeles, CA 90095;; e Department of Psychology, University of California, Los Angeles, CA 90095;; f Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, CA 90095;
Kai-Wei Chang: b Bridging Research Innovation, Training and Education for Science, Research & Policy Center, University of California, Los Angeles, CA 90095;; g Department of Computer Science, University of California, Los Angeles, CA 90095
Jacob G. Foster: a Department of Sociology, University of California, Los Angeles, CA 90095;; b Bridging Research Innovation, Training and Education for Science, Research & Policy Center, University of California, Los Angeles, CA 90095;

Proceedings of the National Academy of Sciences, 2022, vol. 119, issue 10, e2108801119

Abstract: We introduce an approach to identify latent topics in large-scale text data. Our approach integrates two prominent methods of computational text analysis: topic modeling and word embedding. We apply our approach to written narratives of violent death (e.g., suicides and homicides) in the National Violent Death Reporting System (NVDRS). Many of our topics reveal aspects of violent death not captured in existing classification schemes. We also extract gender bias in the topics themselves (e.g., a topic about long guns is particularly masculine). Our findings suggest new lines of research that could contribute to reducing suicides or homicides. Our methods are broadly applicable to text data and can unlock similar information in other administrative databases.

Keywords: natural language processing; mortality surveillance; gender; topic models; word embeddings (search for similar items in EconPapers)
Date: 2022
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