Uncovering Cybercrimes in Social Media through Natural Language Processing

被引：2

作者：

Ramírez Sánchez J. ^{[1
]}

Campo-Archbold A. ^{[1
]}

Zapata Rozo A. ^{[1
]}

Díaz-López D. ^{[1
]}

Pastor-Galindo J. ^{[2
]}

Gómez Mármol F. ^{[2
]}

Aponte Díaz J. ^{[3
]}

机构：

[1] School of Engineering, Science and Technology, Universidad Del Rosario, Carrera 6 # 1 2 C - 16, Bogotá

[2] Faculty of Computer Science, University of Murcia, Campus de Espinardo, Edificio 32, Murcia

[3] Armada Nacional de Colombia, Carrera 54 # 26 - 25, CAN, Bogotá

来源：

Complexity | 2021年 / 2021卷

关键词：

53;

D O I：

10.1155/2021/7955637

中图分类号：

学科分类号：

摘要：

Among 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. An 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. © 2021 Julián Ramírez Sánchez et al.

引用

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