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09:18
Quantification of Drosophila Grooming Behavior
Published on: July 19, 2017
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Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review
Marcelo Leiva-Bianchi1, Nicolas Castillo2, César A Astudillo3
1Laboratory of Methodology, Behavior and Neuroscience, Faculty of Psychology, Talca, Chile.
Scientific Reports
|March 16, 2025
Summary
This study reviewed machine learning (ML) methods for detecting online grooming. Multilayer Perceptron and Support Vector Machine show high accuracy in identifying child sexual abuse risks.
Area of Science:
- Computer Science
- Cybersecurity
- Child Protection
Background:
- Online grooming is a severe form of manipulation and child sexual abuse.
- Effective detection of online grooming is crucial for cybersecurity and child safety.
Purpose of the Study:
- To systematically review and meta-analyze machine learning (ML) methods for online grooming detection.
- To evaluate the performance of various ML algorithms in identifying grooming behaviors.
Main Methods:
- Conducted a systematic review of 33 studies from major academic databases.
- Performed a meta-analysis on 11 ML methods, assessing accuracy, precision, recall, and F1 score.
Main Results:
- Multilayer Perceptron (MLP) achieved the highest accuracy (92%) and precision (81%).
- Support Vector Machine (SVM) demonstrated a balanced performance with high precision (86%), recall (74%), and the highest F1 score (0.79).
Conclusions:
- ML methods, particularly MLP and SVM, are effective in detecting online grooming.
- This research contributes to identifying online predators and enhancing cybersecurity measures against child sexual abuse.
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