Using machine learning to assess rape reports: Sentiment analysis detection of officers' “signaling” about victims' credibility

Rachel E. Lovell, Joanna Klingenstein, Jiaxin Du, Laura Overman, Danielle Sabo, Xinyue Ye and Daniel J. Flannery

Journal of Criminal Justice, 2023, vol. 88, issue C

Abstract: The first of two articles from a larger study whose aim was to teach a computer to detect innuendo (or signaling) about a victim's credibility in incident reports of rape. This study explored the degree of sentiment and subjectivity in the reports and whether these predicted case progression and outcomes.

Keywords: Sexual assault, machine learning; Signaling; Victim credibility; Natural language processing; Sentiment analysis; Attrition (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jcjust:v:88:y:2023:i:c:s0047235223000776

DOI: 10.1016/j.jcrimjus.2023.102106

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