Using Machine Learning to Assess Rape Reports: “Signaling” Words about Victims' Credibility that Predict Investigative and Prosecutorial Outcomes

Authors

Rachel Lovell, Cleveland State UniversityFollow
Joanna Klingenstein
Jiaxin Du
Laura Overman
Danielle Sabo
Xinyue Ye
Daniel J. Flannery

Document Type

Article

Publication Date

9-2023

Publication Title

Journal of Criminal Justice

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.

DOI

10.1016/j.jcrimjus.2023.102107

Recommended Citation

Lovell, Rachel; Klingenstein, Joanna; Du, Jiaxin; Overman, Laura; Sabo, Danielle; Ye, Xinyue; and Flannery, Daniel J., "Using Machine Learning to Assess Rape Reports: “Signaling” Words about Victims' Credibility that Predict Investigative and Prosecutorial Outcomes" (2023). Criminology and Sociology Department Faculty Publications. 2.
https://engagedscholarship.csuohio.edu/cepa_crim_soc_facpub/2

Volume

Volume 88