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Reducing sexual predation and victimization through warnings and awareness among high-risk users

Author

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  • Masanori Takano

    (CyberAgent, Inc.
    Keio University)

  • Mao Nishiguchi

    (The University of Tokyo)

  • Fujio Toriumi

    (The University of Tokyo)

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 randomized controlled trial on a Japanese avatar-based communication application, Pigg Party. High-risk players in the intervention group received warnings and awareness-building messages, while those in the control group did not receive the messages, regardless of their risk level. The trial involved 12,842 high-risk players in the intervention group and 12,844 in the control group for 138 days. The intervention successfully reduced violations and being violated among women for 12 weeks, although the impact on men was limited. These findings contribute to efforts to combat online sexual abuse and advance understanding of criminal psychology.

Suggested Citation

  • Masanori Takano & Mao Nishiguchi & Fujio Toriumi, 2025. "Reducing sexual predation and victimization through warnings and awareness among high-risk users," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-20, August.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00399-3
    DOI: 10.1007/s42001-025-00399-3
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    References listed on IDEAS

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    1. Jaap M J Murre & Joeri Dros, 2015. "Replication and Analysis of Ebbinghaus’ Forgetting Curve," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
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