Ensemble Method for Sexual Predators Identification in Online Chats
Abstract
Cyber grooming is a compelling problem worldwide nowadays and many reports strongly suggested that it becomes very urgent to tackle this problem to protect the children from sexual exploitation. In this study, we propose an effective method for sexual predator identification in online chats based on two-stage classification. The purpose of the first stage is to distinguish predatory conversations from the normal ones while the second stage aims to tell apart between the predator user and the victim within a single predatory conversation. Finally, some unique predators are derived from the second stage result. We investigate several machine learning classifiers including Naive Bayes, Support Vector Machine, Neural Network, Logistic Regression, Random Forest, K-Nearest Neighbors, and Decision Tree with Bag of Words features using several different term weighting methods for this task. We also proposed two ensemble techniques to improve the classification task. The experiment results on PAN12 dataset show that our best method using soft voting based ensemble for first stage and Naive Bayes based method for the second stage obtained an F0.5-score of 0.9348, which would place as number one in the PAN12 competition ranking.
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Some works have explored various phases of grooming to investigate different themes in online conversations, while others focused on predatory conversation detection, predatory identification, or author profiling.
“Some works [28,33,40,42,43] have explored various phases of grooming to investigate the different themes in online conversations, while others focused on predatory conversation detection [49][50][51], predatory identification [41,52,53], or author profiling [44][45][46][47][48].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [52]
Accounting for the frequency of a word in online chat logs can help detect grooming conversations.
“Accounting for the frequency of a word in online chat logs can therefore help detect grooming conversations [51,52,65].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [52]
Some papers used a beta value higher than one to emphasize recall and aim for a lower number of false negative classifications.
“Some papers [52,53] used a β value higher than one that would emphasize recall and aim for a lower number of false negative classifications.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [52]
Few earlier works have tried to identify predators in online chats.
“Few earlier works mentioned above have also tried to identify the predators [51][52][53]71].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [52]
Fauzi and Bours [29] proposed an ensemble model that applies various fusions.
“Fauzi and Bours [29] proposed an ensemble model that applies various fusions.”
From Detecting Online Grooming By Simple Contrastive Chat Embeddings (2023) · cites this paper as [29]
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- Authors
- Fauzi, M. A., & Bours, P.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Venue
- 2020 8th International Workshop on Biometrics and Forensics (IWBF)
- Publisher
- IEEE
- Published
- 2020-04
- Pages
- 1-6
- OpenAlex
- W3033149089
- Cited by
- 34
- References
- 27
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 14 · 2022: 5 · 2021: 8
- FWCI
- 2.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Workshop on Biometrics and Forensics (IWBF) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- online grooming, online chat, child sexual exploitation, two-stage classification, ensemble learning, soft voting, text classification, term weighting, naive Bayes
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