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

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

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.

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: “Signaling” words about victims' credibility that predict investigative and prosecutorial outcomes," Journal of Criminal Justice, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jcjust:v:88:y:2023:i:c:s0047235223000788
    DOI: 10.1016/j.jcrimjus.2023.102107
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    References listed on IDEAS

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    1. Campbell, Bradley A. & Menaker, Tasha A. & King, William R., 2015. "The determination of victim credibility by adult and juvenile sexual assault investigators," Journal of Criminal Justice, Elsevier, vol. 43(1), pages 29-39.
    2. Lovell, Rachel & Luminais, Misty & Flannery, Daniel J. & Bell, Richard & Kyker, Brett, 2018. "Describing the process and quantifying the outcomes of the Cuyahoga County sexual assault kit initiative," Journal of Criminal Justice, Elsevier, vol. 57(C), pages 106-115.
    3. Mourtgos, Scott M. & Adams, Ian T. & Mastracci, Sharon H., 2021. "Improving victim engagement and officer response in rape investigations: A longitudinal assessment of a brief training," Journal of Criminal Justice, Elsevier, vol. 74(C).
    Full references (including those not matched with items on IDEAS)

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