Revisiting Early Detection of Sexual Predators via Turn-level Optimization (2025)

Abstract: 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), 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.

## introduction online grooming is a manipulative tactic where sexual predators establish emotional connections with minors over the internet to exploit them for harmful purposes @xcite . such exploitation is particularly insidious, as it often leads victims to meet their abusers offline voluntarily, resulting in severe risks @xcite . therefore, early and timely intervention within the dialogue is essential for sexual predator detection (spd) @xcite . while conventional spd approaches have primarily focused on post-analysis to identify completed grooming cases for legal prosecution @xcite @xcite , recent efforts have shifted towards early sexual predator detection (espd) @xcite , which aims to detect risks in real-time and intervene before abuse escalates. @xcite ; @xcite framed espd as an early text classification task, and @xcite employed a rule-based classification that continuously evaluates risk using segmented chat (dialogue) data as training data. although various espd methods have been proposed, most rely solely on chat-level labels, which provide weak supervision and fail to identify specific risky utterances. this leads to significant detection errors, such as false alarms in normal conversations (also referred to as dialogues or chats), as illustrated in figure 1 . the reliance on chatlevel labels results in overfitting, causing the model to misclassify normal dialogues as high-risk due to the lack of granular information. the use of chat-level labels also hinders model optimization, preventing it from identifying the precise moment where early detection should occur. to address the instability caused by weak supervision, some previous works have employed count-based methods @xcite , which use a sliding window over the messages of a chat and classify them. however, such rule-based approaches are limited in their ability to create an optimal early detection model, as they lack the flexibility and sophistication to adjust dynamically to real-time risks. in this work, we revisit the task of espd by incorporating turn-level labels, which provide finer granularity and more reliable supervision. we utilize a turn-level labeled dataset @xcite that is annotated based on the luring communication theory (lct) @xcite , capturing the strategies predators use at each stage of the conversation. in addition, we propose a novel speed-controlled reinforcement learning (scorl) method designed to optimize the identification of the optimal early detection point. our scorl framework leverages a speed-control reward function, balancing detection timeliness and accuracy. moreover, we introduce a new benchmark, turn-level espd, to evaluate espd models more effectively. unlike traditional metrics, our proposed metric accounts for both turn-level and chat-level risk factors, ensuring a more precise evaluation. in experiments on the panc dataset @xcite , our scorl method significantly outperforms existing approaches in terms of espd performance. through comprehensive visualizations and case studies on real online grooming conversations, we show that our model not only improves performance quantitatively but also intuitively identifies the optimal points for early intervention. contributions: (1) we propose an effective espd method using reinforcement learning with a novel speed-controlled reward, leveraging turn-level annotations of the predator's entrapment strategy for more granular supervision. (2) we introduce a new benchmark, turn-level espd, which enables more accurate evaluations by addressing the limitations of previous metrics that overlook false positives. ## problem formulation: turn-level espd in this section, we propose a new benchmark, turnlevel espd. we define the turn-level risk label by incorporating the turn-level strategy derived from lct @xcite . we give a formal definition of the task and an evaluation setup for the turn-level espd. ## experimental setup dataset we utilize the publically available panc dataset @xcite , designed for the espd task, which is based on pan12 @xcite and chatcoder2 (cc2) @xcite . the dataset consists of three types of corpus: (1) positive (grooming) full-length chats from cc2, (2) negative (normal) segments from pan12, and (3) positive segments that split the positive full-length chats into parts. our experiments focus on training and evaluating the model using only the positive full-length chats and the negative segments. we use the turnlevel strategy annotations from cc2, as labeled by @xcite , who assigned lct strategy labels to both predator and victim turns when their content aligned with the lct framework. according to pan12, negative segments include cybersex conversations between consenting adults, which serve as hard negative samples since they closely resemble online grooming chats (see appendix b). additionally, they involve cutting the conversations at points where the exchange exceeds 25 minutes or marks a topic shift. therefore, we treat the negative segments as negative full-length chats for training and testing purposes. baseline models we follow the two-tier method proposed by @xcite , representing the current state-of-the-art for the espd task. in tier 1, we fine-tune three variants of bert-bert-large, bert-base @xcite , and mobilebert @xcite )-using the positive and negative segments labeled at the dialogue level, same with @xcite . during evaluation, each model takes a sliding window of messages (window @xmath0. in tier 2, the model monitors the number of positively classified windows in the last 10-window span. if the count surpasses a predefined threshold, referred to as "skepticism" (set to 5 in our setup), the conversation is classified as grooming. we trained the models using a crossentropy loss function with the adam optimizer @xcite . the batch size was set to 16, with a training run for 10 epochs. the learning rates were 3 • 10 -5 for bert-large, and 5 • 10 -3 for both bert-base and mobilebert. sft model our sft model was trained using turn-level risk labels for each turn. the dataset @xcite exhibited significant class imbalance, with negative chats outnumbering positive chats with turn-level risk label by more than 100:1. to mitigate this imbalance, we sampled negative chats for training, prioritizing conversations with more than 100 turns to enrich the turn-level information (see scorl model the scorl model continues processing turns in an episode until either a @xmath1. for each turn, the current utterance and the previous 50 utterances in the conversation history are concatenated, separated by special tokens, forming the current state. the roberta-base encoder, pretrained in the sft step, is used with frozen parameters. only the detection head @xcite ) is trained during this step. the model was trained with a batch size of 32 for 50 epochs, using a learning rate of 2e-4, a discount factor of 0.99, and the adam optimizer. we employed the cosineanneal-ingwarmuprestarts foot_0 scheduler with the following parameters: t 0 = 10, t @xmath2, eta @xmath3.1, t @xmath4, and @xmath5.5. ## conclusion in this work, we revisited espd by incorporating turn-level risk labels to enhance detection accuracy. we introduced the scorl model, designed to identify optimal early detection points using these fine-grained labels, along with a novel evaluation method for turn-level espd. utilizing a dataset annotated with a turn-level risk label based on lct, we first trained a sub-optimal model using sft and then improved it by applying our speedcontrol rewarding mechanism through the rein-force algorithm. our approach outperformed existing sota models, demonstrating the effectiveness of turn-level labels and the speed-control reward. through extensive experiments, we not only validated the quantitative improvements but also showcased the qualitative advantages of our method.