Adolescent online traces can reveal early shifts toward harmful trajectories, yet signals are scattered across text, timing, and peer exposure.To address fragile single-modality profiling, this paper proposes an evidence-linked framework that couples behaviour episode graphs with Transformer-based language representations.First, raw logs are segmented into sessions and converted into a heterogeneous interaction graph to encode rhythm and exposure.Then, event-linked texts are embedded to capture stance and intent cues.Finally, an adaptive fusion learner predicts multi-label psychological trait proxies and a risk score with traceable evidence.Experiments on a de-identified dataset of 8,420 users and 3.6 million events show the proposed method achieves AUC 0.879 and F1 0.821, improving over the strongest single-modality baseline by 0.037 AUC and 0.044 F1, with higher precision 0.833 and recall 0.815.The results indicate robust, interpretable profiling for research-oriented prevention.
Jia Guo (Thu,) studied this question.