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                            Library & Multimedia
 Using machine learning to assess rape reports: Sentiment analysis detection of officers' “signaling” about victims' credibility
NCJ Number
              307440
Journal

*Journal of Criminal Justice* Volume: 88 Issue: 102106 Dated: 2023
Author(s)
          Rachel E. Lovell;
                        Joanna Klingenstein;
                        Jiaxin Du;
                        Laura Overman;
                        Danielle Sabo;
                        Xinyue Ye;
                        Daniel J. Flannery
Date Published

2023
Annotation
              The authors of this paper explored the degree of sentiment and subjectivity in rape reports and whether these predicted case progression and outcomes by teaching a computer to detect signaling via tone that predicted case progression and outcomes; findings indicate that the cases recommended for prosecution were longer and had positive sentiment and positive subjectivity.
Abstract
              In 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, the authorsexplored the degree of sentiment and subjectivity in the reports and whether these predicted case progression and outcomes.The authorstaught a computer to detect signaling via tone that predicted case progression and outcomes. Findings indicate that the cases recommended for prosecution were longer and had positive sentiment and positive subjectivity. Cases not recommended for prosecution were shorter with more neutral statements of “fact” or observations. Implications and recommendations for improved, less biased report writing are provided. The authorsemployed machine learning, specifically sentiment analysis to assess sentiment (opinion) and subjectivity of textual content. The sample consists of 5638 incident reports of rape with a sexual assault kit from a U.S., urban Midwestern jurisdiction. Sentiment was detected, tended to skew near neutral/slightly negative and more subjective, and predicted case progression and outcomes, but was not quite what was expected.(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: January 1, 2023
    
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