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                            Funding & Awards
 Using Sentiment Analysis and Topic Modeling in Assessing the Impact of Police Signaling on Investigative and Prosecutorial Outcomes in Sexual Assault Reports
Award Information
Awardee
              CASE WESTERN RESERVE UNIVERSITY
Award #
              2018-VA-CX-0002
Funding Category
              Competitive Discretionary
Location
                    CLEVELAND, OH
Congressional District
              11
Status
              Closed
Funding First Awarded
              2018
Total funding (to date)
              $714,199
Original Solicitation
              NIJ FY18 Research and Evaluation on Violence Against Women: Sexual Violence, Intimate Partner Violence, Stalking, and Teen Dating Violence
Description of original award (Fiscal Year 2018, $714,199)
The purpose of this study is to identify signaling in narratives of police officers sexual assault reports that influence subsequent decision making, case flow, and attrition. Signaling is defined as information conveyed by responding officers in the narratives of police reports regarding a victims creditability and rape-myth adherence. The goal is to better understand if and how responding officers written reports in sexual assault cases impact investigating officers decision making and how cases proceed, or fail to proceed, in the criminal justice process. The objective of the proposed study is to explore the first step in the investigative process to reveal facilitators and barriers to sexual assault cases reaching a successful disposition. The team will use text mining and machine learning methods using state-of-the-art software for natural language processing and advanced statistical analyses to evaluate the narratives of over 6,300 police reports of sexual assaults where victims had sexual assault kits collected in one large, urban jurisdiction over the span of nearly two decades (1993 through 2009). These reports will be analyzed using two computational methods: 1) topic modeling, a statistical approach to identifying and coding latent topics and 2) sentiment analysis, which involves identifying positive and negative opinions, emotions, and attitudes. The three research questions driving this study include: to what extent do sentiments in the responding officers narratives reveal positive or negative signaling of victims credibility; to what extent are the topics and sentiments in the responding officers narratives different in cases with increased investigative activity compared to those with less; to what extent are both the topics and sentiments in the responding officers narratives different for cases that were successfully investigated and prosecuted compared to those that were not?
"Note: This project contains a research and/or development component, as defined in applicable law," and complies with Part 200 Uniform Requirements - 2 CFR  200.210(a)(14).
ca/ncf.



  
Grant-Funded Publications
        
                      Using machine learning to assess rape reports: “Signaling” words about victims' credibility that predict investigative and prosecutorial outcomes
                  Using machine learning to assess rape reports: Sentiment analysis detection of officers' “signaling” about victims' credibility
                  What's Hiding in the Text? Analyzing Sexual Assault Police Narratives for "Signaling"
                  Using Sentiment Analysis and Topic Modeling in Assessing the Impact of Police Signaling on Investigative and Prosecutorial Outcomes in Sexual Assault Reports
                  Examining Walking-Waiting Sexual Assaults from Previously Untested Sexual Assault Kits: The Intersection of Stranger and Outdoor Sexual Assaults

  
Date Created: September 27, 2018
    
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