Reducing sexual predation and victimization through warnings and awareness among high-risk users
Masanori Takano, Mao Nishiguchi, Fujio Toriumi

TL;DR
This study shows that warning high-risk users on an online platform can reduce sexual predation and victimization, especially among women.
Contribution
A novel strategy using routine activity theory and machine learning to warn high-risk users and reduce online sexual predation.
Findings
The intervention reduced violations and victimization among women for 12 weeks.
The impact of the intervention was limited for men.
High-risk users were identified using a machine learning model analyzing social networks and monitoring data.
Abstract
Online sexual predators target children by building trust, creating dependency, and arranging meetings for sexual purposes. This poses a significant challenge for online communication platforms that strive to monitor and remove such content and terminate predators’ accounts. However, these platforms can only take such actions if sexual predators explicitly violate the terms of service, not during the initial stages of relationship-building. This study designed and evaluated a strategy to prevent sexual predation and victimization by delivering warnings and raising awareness among high-risk individuals based on the routine activity theory in criminal psychology. We identified high-risk users as those with a high probability of committing or being subjected to violations, using a machine learning model that analyzed social networks and monitoring data from the platform. We conducted a…
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Taxonomy
TopicsSexuality, Behavior, and Technology · Gender, Feminism, and Media · Psychopathy, Forensic Psychiatry, Sexual Offending
