Supervised Machine Learning Approach Discovers Protective Sequence for Avoiding Sexual Victimization in Criminal Suit Documents

Abstract

Previous research has studied effective self-protective behaviors, such as a victim’s physical resistance leading to the avoidance of sexual victimization. However, there are few studies on effective self-protective behavioral sequences, such as an offender’s physical violence followed by the victim’s physical resistance. Our study aims to clarify these sequences through a supervised machine learning approach. The samples consisted of 88 official documents on sexual assaults involving women, committed by male offenders incarcerated in a Japanese local prison. These crimes were classified as completed or attempted cases based on judges’ evaluations. All phrases in each crime description were also partitioned and coded according to the Japanese Penal Code. The support vector machine identified the most likely sequences of behaviors to predict completed and attempted cases. Approximately 90% of cases were correctly predicted through the identification of behavior sequences. The sequence involving an offender’s violence followed by the victim’s physical resistance predicted attempted sexual assault. However, the sequence involving a victim’s general resistance followed by the offender’s violence predicted completed sexual assault. Victims’ and offender’s behaviors need to be interpreted from behavioral sequence perspectives rather than a single action perspective. The supervised machine learning methodologies may extract self-protective behavioral sequences in documents more effectively than other methodologies. The self-protective sequence is a fundamental part of resistance during sexual assault. Training focused on protective sequence contributes to the improvement of resistance training and rape avoidance rates.