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                            Library & Multimedia
 Using machine learning to assess rape reports: “Signaling” words about victims' credibility that predict investigative and prosecutorial outcomes
NCJ Number
              307441
Journal

*Journal of Criminal Justice* Volume: 88 Issue: 102107 Dated: September 2023
Author(s)
          Rachel E. Lovell;
                        Joanna Klingenstein;
                        Jiaxin Du;
                        Laura Overman;
                        Danielle Sabo;
                        Xinyue Ye;
                        Daniel J. Flannery
Date Published

September 2023
Annotation
              In 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, the authors explored whether words expressed or not expressed, intentionally or not, influenced case progression and outcomes. As hypothesized, predictive phrases were different in cases that stalled earlier.
Abstract
              In 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, the authors explored if the words expressed or not expressed, intentionally or not, influenced case progression and outcomes. As hypothesized, predictive phrases were different in cases that stalled earlier. Cases not recommended for prosecution lacked detail and more heavily mentioned: (in)actions of victims, actions that stall cases, and procedural words. Reports where victims were not believed or unfounded were similarly vague, procedural, and terse. Cases recommended for prosecution predictively mentioned suspects and the rape statute. The authors taught a computer to detect signaling via words that were predictive of case progression and outcomes. Negative signals about a victim's credibility often presented as unqualified statements of “fact” or observations or procedural words, indicating a focus on the process vs. victim or suspect. Implications and recommendations are provided, including how unqualified doubts about victims' credibility have substantial public safety consequences. The authors employed machine learning, specifically text classification, to identify predictive phrases. Sample consisted of 5638 incident reports of rape with a sexual assault kit from a U.S., urban Midwestern jurisdiction. (Published Abstract Provided)
      
  
          Additional Details
Grant Number(s)
        2018-VA-CX-0002


  
  
        
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        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Using Sentiment Analysis and Topic Modeling in Assessing the Impact of Police Signaling on Investigative and Prosecutorial Outcomes in Sexual Assault Reports
    
  
  
Date Published: September 1, 2023
    
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