Using machine learning to assess rape reports: “Signaling” words about victims' credibility that predict investigative and prosecutorial outcomes

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 second 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 if the words expressed or not expressed, intentionally or not, influenced case progression and outcomes.

Keywords: Sexual assault; Machine learning; Signaling; Victim credibility; Text classification (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:s0047235223000788

DOI: 10.1016/j.jcrimjus.2023.102107

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