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Cited by 15Hate Speech and Cyberbullying DetectionSpam and Phishing DetectionText and Document Classification Technologies

Classifying cybergrooming for child online protection using hybrid machine learning model

Isaza, Gustavo, Muñoz, Fabián, Castillo, Luis, Buitrago, Felipe·2022·Neurocomputing
DOI 10.1016/j.neucom.2021.08.148
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Crossref · 10.1016/j.neucom.2021.08.148

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  • Most computer science literature focuses on detecting perpetrators' behaviors using machine learning and natural language processing techniques.

    “To combat cybergrooming, researchers have developed numerous computational approaches, with most computer science literature focuses on detecting perpetrators' behaviors using machine learning (ML) and natural language processing (NLP) techniques (e.g., [5, 16-19, 3234, 42, 44, 48, 59, 62, 72, 76-78]).”

    From From Vulnerable to Resilient: Examining Parent and Teen Perceptions on How to Respond to Unwanted Cybergrooming Advances (2026) · cites this paper as [48]

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Authors
Isaza, G., Muñoz, F., Castillo, L., & Buitrago, F.
Affiliations
  • Universidad de Caldas (CO)
  • Universidad Tecnológica de Pereira (CO)
  • Universidad Autonoma de Manizales (CO)
Corresponding author
Isaza, Gustavo
ORCID
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2022-05
Volume
484
Pages
250-259
ISSN
0925-2312 (ISSN-L)
DOI
10.1016/j.neucom.2021.08.148
OpenAlex
W3210690950
Cited by
15
References
1
Citations by year
2026: 1 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 2
FWCI
0.80
Type
journal article
Language
en
Open access
closed
Licence
Locations
Neurocomputing (publishedVersion)
Funders
Universidad de Caldas
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords
cyberbullying, child online protection, cyberbullying detection, text classification, convolutional neural networks, natural language processing, hybrid machine learning, false positives, abnormal behavior detection, child online safety

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