A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

Abstract

Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

Intention Recognition in Digital Forensics: Systematic Review

“…The landscape within the subdomain has undergone a significant shift, transitioning from a focus primarily on network security (in the case of logic-based approaches) to encompassing a broader range of cases [10,27]. Additionally, researchers have delved into identifying intents related to social media utilization, as explored by [25,26]. Notably, the work by T. Li et al [27] stands out as it operates at a higher level of plan recognition, while the remaining studies primarily address intent or goal recognition.…”

“…Considering the fact that, criminals often use slang expressions to communicate, plan, and execute their illicit activities online, to capture the hidden meanings and intention behind these expressions, Ricardo R. de Mendonça et al [25] proposed a novel framework to detect and classify criminal intentions in social media texts ciphered with slangs. The framework, called Ontology-Based Framework for Criminal Intention Classification (OFCIC), combines Semantic Web, Semiotics, Speech Act Theory, and Machine Learning techniques to select, decipher, and classify posts with criminal slang expressions according to their illocutionary classes, which are the types of speech acts that convey the speaker's intention.…”

Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions

“…With the sudden rise in social media, micro-blogging sites like Twitter have already started detecting, censoring, and blocking people with "hateful" behaviour. In [ 10 ], a model is presented to detect criminal acts on Social Media Websites with 8,835,290 tweets as a corpus. On the other hand, the [ 11 ] presents non-verbal cues of malicious intent.…”

Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

“…Twitter is one of the social media platforms based on sharing, uploading user opinions and providing information about new studies, interests, etc. [34][35][36]. There are many research articles related to Twitter classification in various goals.…”