Grooming detection using fuzzy-rough feature selection and text classification
Open accessAbstract
Online child grooming detection has recently attracted intensive research interests from both the machine learning community and digital forensics community due to its great social impact. The existing data-driven approaches usually face the challenges of lack of training data and the uncertainty of classes in terms of the classification or decision boundary. This paper proposes a grooming detection approach in an effort to address such uncertainty based on a data set derived from a publicly available profiling data set. In particular, the approach firstly applies the conventional text feature extraction approach in identifying the most significant words in the data set. This is followed by the application of a fuzzy-rough feature selection approach in reducing the high dimensions of the selected words for fast processing, which at the same time addressing the uncertainty of class boundaries. The experimental results demonstrate the efficiency and efficacy of the proposed approach in detecting child grooming.
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Some papers attempted to consider this setting and have tried to address this as an imbalanced data problem.
“Some papers attempted to consider this setting and have tried to address this as an imbalanced data problem [62,72,73,86].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [72]
The Data Set section (page unavailable) describes the reconstructed grooming dataset as highly imbalanced, and the Impact of feature dimensions section evaluates the approach in light of that imbalance. But the Introduction and Conclusion say the proposed fuzzy-rough method addresses uncertainty in natural-language conversations, not class imbalance as the problem it was designed to solve. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe necessity of pertinent data leads to the challenge of a highly imbalanced dataset in grooming detection, where the amount of predatory conversations data is much lower than everyday conversations data.
“The necessity of pertinent data also leads to the challenge of the highly imbalanced dataset in grooming detection, where the amount of predatory conversations data is much lower than everyday conversations data [62,72,85].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [72]
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Razi, A., Kim, S., Alsoubai, A., Stringhini, G., Solorio, T., De Choudhury, M., & Wisniewski, P. J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479609 · Cited by 95 · see citing works
Cyber-aggression, Cyberbullying, and Cyber-grooming: A Survey and Research Challenges
Mladenović, M., Ošmjanski, V., & Stanković, S. V. (2022) · ACM Computing Surveys · doi:10.1145/3424246 · Cited by 79 · see citing works
Online grooming detection: A comprehensive survey of child exploitation in chat logs
Borj, P. R., Raja, K., & Bours, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110039 · Cited by 38 · see citing works
Milon-Flores, D. F., & Cordeiro, R. L. F. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.108017 · Cited by 29 · see citing works
- Authors
- Zuo, Z., Li, J., Anderson, P., Yang, L., & Naik, N.
- Affiliations
- Northumbria University (GB)
- Ministry of Defence (GB)
- Venue
- 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-8
- ISSN
- 1098-7584 (ISSN-L)
- OpenAlex
- W2883935747
- Cited by
- 39
- References
- 1
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 5 · 2022: 6 · 2021: 10 · 2020: 3 · 2019: 7 · 2018: 4
- FWCI
- 2.45
- Type
- conference paper
- Language
- en
- Open access
- Locations
- IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (publishedVersion)
- Northumbria Research Link (Northumbria University) (submittedVersion)
- Teesside University Research Portal (Teesside University) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- text classification, feature extraction, dimensionality reduction, child online safety
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