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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
de Mendonca, Ricardo Resende [1] ; de Brito, Daniel Felix [2] ; Rosa, Ferrucio de Franco [2, 1] ; dos Reis, Julio Cesar [3] ; Bonacin, Rodrigo [2, 1]
Total Authors: 5
Affiliation:
[1] UNIFACCAMP, Comp Sci Master Program, BR-13231230 Campo Limpo Paulista, SP - Brazil
[2] Informat Technol Ctr Renato Archer, BR-13069901 Campinas, SP - Brazil
[3] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INFORMATION; v. 11, n. 3 MAR 2020.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 17/02325-5 - EvOLoD: linked data evolution on the Semantic Web
Grantee:Julio Cesar dos Reis
Support type: Research Grants - Young Investigators Grants