Amongst the myriad of applications of Natural Language Processing (NLP), assisting Law Enforcement Agencies (LEA) in detecting and preventing cybercrimes is one of the most recent and promising ones. The promotion of violence or hate by digital means is considered a cybercrime as it leverages the cyberspace to support illegal activities in the real world. The paper at hand proposes a solution that uses Neural Network (NN) based NLP to monitor suspicious activities in social networks allowing us to identify and prevent related cybercrimes. A LEA can find similar posts grouped in clusters, then determine their level of polarity, and identify a subset of user accounts that promote violent activities to be reviewed extensively as part of an effort to prevent crimes and specifically Hostile Social Manipulation (HSM). Different experiments were also conducted to prove the feasibility of the proposal.

This work would not have been possible without the inestimable contribution of:

• Julián Ramírez Sánchez
• Alejandra Campo-Archbold
• Andrés Zapata Rozo
• Daniel O. Díaz López
• Javier Pastor Galindo
• Julián Aponte Díaz

Julián Ramírez Sánchez, Alejandra Campo-Archbold, Andrés Zapata Rozo, Daniel O. Díaz López, Javier Pastor Galindo, Félix Gómez Mármol, Julián Aponte Díaz, «Uncovering cybercrimes in social media through natural language processing«, Special Issue on Deep Learning Methods Applied to Complex Big Data Analysis 2021, Complexity, vol. 2021, no. 7955637, pp. 1-15, 2021

• Journal: Complexity
• Category: Mathematics, Interdisciplinary Applications
• Rank: 43/108
• Quartile: Q2
• Impact Factor: 2.121