Article: Psychological trait mining of juvenile offending from large language models and online behaviour analytics Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.67 pp.50 - 67 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: Psychological trait mining of juvenile offending from large language models and online behaviour analytics

Authors: Jia Guo

Addresses: School of Criminology, People's Public Security University of China, Beijing, 100038, China

Abstract: 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.

Keywords: large language models; network behaviour analysis; juvenile delinquency; psychological trait mining; multimodal fusion; behaviour graph learning; risk profiling.

DOI: 10.1504/IJICT.2026.154214

International Journal of Information and Communication Technology, 2026 Vol.27 No.67, pp.50 - 67

Received: 05 Feb 2026
Accepted: 06 Mar 2026
Published online: 16 Jun 2026 *

Title: Psychological trait mining of juvenile offending from large language models and online behaviour analytics

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