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

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

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.

Suggested Citation

  • Lovell, Rachel E. & Klingenstein, Joanna & Du, Jiaxin & Overman, Laura & Sabo, Danielle & Ye, Xinyue & Flannery, Daniel J., 2023. "Using machine learning to assess rape reports: Sentiment analysis detection of officers' “signaling” about victims' credibility," Journal of Criminal Justice, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jcjust:v:88:y:2023:i:c:s0047235223000776
    DOI: 10.1016/j.jcrimjus.2023.102106
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    References listed on IDEAS

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    1. Mourtgos, Scott M. & Adams, Ian T., 2019. "The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
    2. Tellis, Katharine M. & Spohn, Cassia C., 2008. "The sexual stratification hypothesis revisited: Testing assumptions about simple versus aggravated rape," Journal of Criminal Justice, Elsevier, vol. 36(3), pages 252-261, July.
    3. Bouffard, Jeffrey A., 2000. "Predicting type of sexual assault case closure from victim, suspect, and case characteristics," Journal of Criminal Justice, Elsevier, vol. 28(6), pages 527-542.
    Full references (including those not matched with items on IDEAS)

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