ArticleBehavioral sciences & the law
Decoding Disbelief: Using Natural Language Processing's Sentiment Analysis to Assess 24 Years of Unfounded Rape Reports Narratives.
Article in Behavioral sciences & the law. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Decoding Disbelief: Using Natural Language Processing's Sentiment Analysis to Assess 24 Years of Unfounded Rape Reports Narratives.Behavioral sciences & the lawArticle
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3 authors.
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Abstract
Rape myths, including the belief that victims frequently lie, contribute to barriers in justice, such as the disproportionate use of the "unfounded" classification-where, following an investigation, it is determined no crime occurred. This study analyzes rape report narratives tied to previously untested sexual assault kits (N = 5638) from a large, urban Midwestern (US) jurisdiction, focusing on differences in narratives deemed unfounded or where officers expressed victim lying/doubt. Using natural language processing's sentiment analysis, we assessed tone (via polarity and subjectivity) and word counts. Results showed that unfounded narratives were shorter and more negatively written than others but did not differ in subjectivity. Victim lied/doubted narratives showed no significant difference in polarity, subjectivity, or length compared to others. These findings highlight how bias can manifest in written narratives, potentially influencing case outcomes. Addressing these biases through improved report writing and limiting the misuse of the unfounded classification is essential to support victims' pathways to justice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.