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Unveiling Conversational Patterns: Intent Classification and Clustering in Reddit’s Firearm Trade Community

Maria Makrynioti, Chaido Porlou,Anastasios Alexiadis,Georgios Stavropoulos,Konstantinos Votis, Dimitrios Tzovaras

2023 IEEE International Conference on Big Data (BigData)(2023)

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Abstract
Online discussion boards have become a tool for traffickers to widen their reach in the illegal firearm trade. This provides opportunities for authorities to detect firearm trafficking networks, as well as interdict arms shipments arranged by such networks. The present work offers a fresh solution to the pressing problem of spotting dubious firearm transaction chats on online discussion boards. The suggested methodology integrates state-of-the-art capabilities, such as natural language understanding and unsupervised clustering methods, to support the creation of more efficient cybercrime solutions. The necessity to target and flag questionable discussions about the trading of firearms in Internet forums is the main issue this work addresses. The major claim in this study centers on the use of intent recognition sequence patterns within dialogues and unsupervised clustering of such sequences. This method groups conversations based on the general direction and subject. The research methodology involves several key steps: i) data collection and manual annotation from the subreddit r/GunDeals, chosen due to its proximity to the primary subject of illicit firearm tracking, ii) training an intent classification Transformer-based model to generate intent sequences for each conversation, iii) preprocessing the intent sequences for clustering and encoding them using a polynomial method. iv) implementing clustering techniques while tuning various hyperparameters to optimize the results. The proposed methodology effectively classifies conversations into meaningful clusters, providing actionable insights. This research contributes to the body of knowledge by presenting a novel approach to detecting suspicious firearm trade conversations online. The practical implications are significant, as this work can be leveraged by law enforcement agencies to enhance their Internet scanning capabilities. The ability to efficiently process large volumes of text conversations and flag specific content can aid in addressing illicit firearm trading more effectively.
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Key words
NLP,NLU,Artificial Intelligence,intent classification,conversational analysis,firearm,Reddit,clustering
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