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Cited by 21Hate Speech and Cyberbullying DetectionText and Document Classification TechnologiesSpam and Phishing Detection

Detecting sexual predators in chats using behavioral features and imbalanced learning

CARDEI, CLAUDIA, REBEDEA, TRAIAN·2017·Natural Language Engineering
DOI 10.1017/s1351324916000395
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Abstract

This paper presents a system developed for detecting sexual predators in online chat conversations using a two-stage classification and behavioral features. A sexual predator is defined as a person who tries to obtain sexual favors in a predatory manner, usually with underage people. The proposed approach uses several text categorization methods and empirical behavioral features developed especially for the task at hand. After investigating various approaches for solving the sexual predator identification problem, we have found that a two-stage classifier achieves the best results. In the first stage, we employ a Support Vector Machine classifier to distinguish conversations having suspicious content from safe online discussions. This is useful as most chat conversations in real life do not contain a sexual predator, therefore it can be viewed as a filtering phase that enables the actual detection of predators to be done only for suspicious chats that contain a sexual predator with a very high degree. In the second stage, we detect which of the users in a suspicious discussion is an actual predator using a Random Forest classifier. The system was tested on the corpus provided by the PAN 2012 workshop organizers and the results are encouraging because, as far as we know, our solution outperforms all previous approaches developed for solving this task.

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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 [62]

  • The cost-sensitive technique performed best when testing on the PAN2012 dataset for predator detection, where a cost matrix defines the penalty for misclassifying a sample.

    “The cost-sensitive technique performed best when testing on the PAN2012 dataset for predator detection, where a cost matrix defines the penalty for misclassifying a sample.”

    From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [62]

  • The 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 [62]

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Authors
CARDEI, C., & REBEDEA, T.
Affiliations
Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
ORCID
REBEDEA, TRAIAN
Venue
Natural Language Engineering
Publisher
Cambridge University Press (CUP)
Published
2017-01-31
Volume
23
Issue
4
Pages
589-616
ISSN
  • 1351-3249 (ISSN-L)
  • 1469-8110
DOI
10.1017/s1351324916000395
OpenAlex
W2585232420
Cited by
21
References
41
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 11 · 2020: 1 · 2019: 1 · 2017: 1
FWCI
0.49
Type
journal article
Language
en
Open access
closed
Licence
www.cambridge.org/core/terms (unspecified)
Locations
Natural Language Engineering (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
child sexual exploitation, behavioral characteristics, imbalanced learning, two-stage classification, text classification, support vector machine, random forest

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