Understanding the social construction of juvenile delinquency: insights from semantic analysis of big-data historical newspaper collections

Yu Zhang (), Adam Davies () and ChengXiang Zhai
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Yu Zhang: California State University, Fresno
Adam Davies: University of Illinois Urbana-Champaign
ChengXiang Zhai: University of Illinois Urbana-Champaign

Journal of Computational Social Science, 2024, vol. 7, issue 2, No 1, 1095-1137

Abstract: Abstract Massive historical newspaper collections contain rich information about the historical development of social issues and constitute a unique resource for studying the social construction of issues such as juvenile delinquency. However, manual analysis of millions of pages of newspaper articles is infeasible. In this paper, we propose a suite of computational methods, including cross-context lexical analysis, dynamic semantic analysis, and valence analysis, to facilitate the study of historical social construction. We apply these methods to ProQuest Historical Newspapers $$^{\textrm{TM}}$$ TM collection in the period of 1790–2006 to study the social construction of juvenile delinquency over this period. Our results show that the proposed methods are effective in revealing insights regarding the social construction of juvenile delinquency, leading to a better understanding of this complex issue and specific hypotheses for further study. Overall, our study shows the great promise of leveraging natural language processing techniques for analyzing historical news data to study social construction of societal issues.

Keywords: Juvenile delinquency; Social construction; Historical newspapers; Natural language processing; Word embeddings; Computational semantics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-024-00254-x

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