Decoding Disbelief: Using Natural Language Processing's Sentiment Analysis to Assess 24 Years of Unfounded Rape Reports Narratives

解读怀疑:运用自然语言处理的情感分析评估24年来未经证实的强奸报案叙述

阅读:2

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。