通过警告和提高高风险用户的意识,减少性掠夺和受害
Masanori Takano1,2, Mao Nishiguchi3, Fujio Toriumi3
1Multidisciplinary Information Science Center, CyberAgent, Inc., Tokyo, Japan.
Journal of computational social science
|July 2, 2025
概括
这项研究开发了一项干预措施,通过警告高风险用户来打击在线性掠夺. 该策略成功地减少了日本在线游戏中女性的受害者,突出了其在线安全潜力.
科学领域:
- 网络心理学 网络心理学
- 犯罪心理学 犯罪心理学
- 在线安全研究 在线安全研究
背景情况:
- 在线性掠夺者利用建立信任的阶段,避免被发现,直到发生明显的违规行为.
- 现有的平台调节在在线掠夺的新生关系建设阶段努力干预.
- 常规活动理论为理解和预防犯罪机会提供了一个框架.
研究的目的:
- 设计和评估基于常规活动理论的干预策略,以防止在线性掠夺和受害.
- 使用机器学习识别易受或参与掠夺性行为的高风险用户.
- 评估警告和宣传信息在缓解在线性虐待方面的有效性.
主要方法:
- 一个机器学习模型分析了社交网络和平台数据,以识别高风险用户.
- 一项随机对照试验在日本基于化身的通信应用程序Pigg Party上进行.
- 12,842名高风险用户在138天内收到警告 (干预组); 12,844人没有收到警告 (对照组).
主要成果:
- 这项干预措施在12周内显著减少了女性使用者的性侵犯和受害事件.
- 该策略对减少男性使用者的违规行为或受害者行为产生了有限的影响.
- 这些发现证明了在线环境中主动,基于意识的干预措施的有效性.
结论:
- 积极的警告和宣传活动可以有效地减少在线性掠夺和受害,特别是在弱势群体.
- 机器学习是识别在线通信平台中面临风险的个人的可行工具.
- 需要进一步的研究,以优化针对不同用户群体的干预措施,包括男性.
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