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

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.