The growing use of digital communication platforms has given rise to various criminal activities, such as grooming and drug dealing, which pose significant challenges to law enforcement and forensic experts. This paper presents a supervised keyphrase extraction approach to detect relevant information in high-volume chat logs involving grooming and drug dealing for forensic analysis. The proposed method, JointKPE++, builds upon the JointKPE keyphrase extractor by employing improvements to handle longer texts effectively. We evaluate JointKPE++ using BERT-based pre-trained models on grooming and drug dealing datasets, including BERT, RoBERTa, SpanBERT, and BERTimbau. The results show significant improvements over traditional approaches and demonstrate the potential for JointKPE++ to aid forensic experts in efficiently detecting keyphrases related to criminal activities.
@article{arxiv.2311.04905,
title = {Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis},
author = {Jeovane Honório Alves and Horácio A. C. G. Pedroso and Rafael Honorio Venetikides and Joel E. M. Köster and Luiz Rodrigo Grochocki and Cinthia O. A. Freitas and Jean Paul Barddal},
journal= {arXiv preprint arXiv:2311.04905},
year = {2024}
}
Comments
Accepted for presentation at the 22nd IEEE International Conference on Machine Learning and Applications (ICMLA) 2023