用逐轮风险评估提前发现网络诱拐，干预更及时。
Revisiting Early Detection of Sexual Predators via Turn-level Optimization
- 基于诱捕理论构建逐轮风险标签，捕捉施害者逐步诱导过程。
- 设计速度控制奖励函数，在准确率与响应速度间取得平衡。
- 提出新评估指标，揭示旧方法在聊天层级上的局限性。
网络诱拐是严重社会威胁，施害者通过渐进式言语操控逐步引诱儿童受害者。及时干预对主动保护至关重要，但以往方法因依赖聊天层级的风险标签，导致对高风险语句的监督弱化，难以确定最佳干预时机。本文提出速度控制强化学习（SCoRL），基于诱捕通信理论（LCT）设计逐轮风险标签，以捕捉施害者每一轮对话中的诱骗行为。进一步构建新型速度控制奖励函数，权衡检测速度与准确性，实现最优干预点识别。同时引入逐轮评估指标，揭示传统聊天层级指标的不足。实验表明，SCoRL能有效提前阻断网络诱拐，提供更具前瞻性与时效性的解决方案。深入分析显示，该方法不仅提升性能，还能直观定位早期干预的最佳时机。
原文摘要 · Abstract (English)
Online grooming is a severe social threat where sexual predators gradually entrap child victims with subtle and gradual manipulation. Therefore, timely intervention for online grooming is critical for proactive protection. However, previous methods fail to determine the optimal intervention points (i.e., jump to conclusions) as they rely on chat-level risk labels by causing weak supervision of risky utterances. For timely detection, we propose speed control reinforcement learning (SCoRL) (The code and supplementary materials are available at https://github.com/jinmyeongAN/SCoRL), incorporating a practical strategy derived from luring communication theory (LCT). To capture the predator's turn-level entrapment, we use a turn-level risk label based on the LCT. Then, we design a novel speed control reward function that balances the trade-off between speed and accuracy based on turn-level risk label; thus, SCoRL can identify the optimal intervention moment. In addition, we introduce a turn-level metric for precise evaluation, identifying limitations in previously used chat-level metrics. Experimental results show that SCoRL effectively preempted online grooming, offering a more proactive and timely solution. Further analysis reveals that our method enhances performance while intuitively identifying optimal early intervention points.
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