Detecting predatory conversations in social media by deep convolutional neural networks
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Some works have used Word2vec and GloVe feature vectors for detecting sexual predatory conversations.
“Some works have used Word2vec, and GloVe feature vectors for detecting sexual predatory conversations [51,64,68].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [64]
Pre-trained word embeddings trained using general documents such as Google News data are unsuitable for sexual predatory detection because chat logs contain many out-of-vocabulary and slang words.
“However, the pre-trained word embeddings trained using general documents such as Google News data are unsuitable for sexual predatory detection problems because chat logs contain many out-of-vocabulary and slang words [64].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [64]
Cites
Recurrent neural network based language model
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Neural Networks: Tricks of the Trade: Second Edition
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Opinion Mining with Deep Recurrent Neural Networks
Irsoy, O., & Cardie, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1080 · Cited by 384 · see citing works
Detecting child grooming behaviour patterns on social media
Cano, A. E., Fernandez, M., & Alani, H. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-13734-6_30 · Cited by 79 · see citing works
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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
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
Alsoubai, A., Song, J., Razi, A., Naher, N., De Choudhury, M., & Wisniewski, P. J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555136 · Cited by 36 · see citing works
Ensemble Method for Sexual Predators Identification in Online Chats
Fauzi, M. A., & Bours, P. (2020) · 2020 8th International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf49977.2020.9107945 · Cited by 34 · 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
Detection of Cyber Grooming in Online Conversation
Bours, P., & Kulsrud, H. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035090 · Cited by 27 · see citing works
Detecting Online Grooming By Simple Contrastive Chat Embeddings
Rezaee Borj, P., Raja, K., & Bours, P. (2023) · Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics · doi:10.1145/3579987.3586564 · Cited by 11 · see citing works
Detecting Sexual Predatory Chats by Perturbed Data and Balanced Ensembles
Borj, P. R., Raja, K., & Bours, P. (2021) · 2021 International Conference of the Biometrics Special Interest Group (BIOSIG) · doi:10.1109/biosig52210.2021.9548303 · Cited by 13 · see citing works
Identifying Sexual Predators in Chats Using SVM and Feature Ensemble
Fauzi, M. A., Wolthusen, S., Yang, B., Bours, P., & Yeng, P. (2023) · 2023 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC) · doi:10.1109/etncc59188.2023.10284950 · Cited by 4 · see citing works
A Survey of Generative AI for Detecting Pedophilia Crimes
Silva, F., Silva, R. R., & Bernardino, J. (2025) · Applied Sciences · doi:10.3390/app15137105 · Cited by 4 · see citing works
Eilifsen, T. N., Shrestha, B., & Bours, P. (2023) · 2023 21st International Conference on Emerging eLearning Technologies and Applications (ICETA) · doi:10.1109/iceta61311.2023.10343952 · Cited by 3 · see citing works
- Authors
- Ebrahimi, M., Suen, C. Y., & Ormandjieva, O.
- Affiliations
- Concordia University (CA)
- Corresponding author
- Ebrahimi, Mohammadreza
- Venue
- Digital Investigation
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 18
- Pages
- 33-49
- ISSN
- 1742-2876 (ISSN-L)
- OpenAlex
- W2465152470
- Cited by
- 78
- References
- 5
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 10 · 2022: 7 · 2021: 11 · 2020: 10 · 2019: 11 · 2018: 4 · 2017: 6 · 2016: 1
- FWCI
- 6.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Digital Investigation (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (N00009)
- 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
- chat logs, online child sexual exploitation, deep convolutional neural networks, text classification, pre-trained word vectors, GPU parallel computing, law enforcement, social media
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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