On preprocessing the data for improving sexual predator detection: Anonymous for review
Abstract
Sexual predator detection and predatory message identification are critical to avoid under-aged children from being abused online. In this paper, we investigate different feature extraction approaches for predatory detection. While the previous results indicate good accuracy on predatory conversation detection, there is a missing investigation on the robustness of feature space. Further, we also show the impact of preprocessing on data to improve the performance of predator identification and predatory message classification. Various types of the bag of words features, including binary, term frequency, and TF-IDF representation are investigated on the publicly available PAN 2012 competition dataset for predator identification. Further, to cover the relationship between the words in the text analysis, the GloVe feature set is also investigated for word embedding features. With the set of preprocessing of data, we illustrate the improvement in detecting predatory conversation with an accuracy of 0.994 and F1-score of 0.964.
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[51] used Term Count as one of the feature vectors for analyzing sexual predatory conversations, and the results showed that it could detect online grooming conversations with good performance.
“[51] used TC as one of the feature vectors for analyzing the sexual predatory conversations, and the results showed that it could detect online grooming conversations with good performance.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [51]
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 [51]
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 [51]
Preprocessing could increase the performance of the models for sexual predatory conversation detection.
“The authors showed how preprocessing could increase the performance of the models for sexual predatory conversation detection [51].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [51]
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 [51]
Cites
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
“Cause ur special”: Understanding trust and complimenting behaviour in online grooming discourse
Lorenzo-Dus, N., & Izura, C. (2017) · Journal of Pragmatics · doi:10.1016/j.pragma.2017.01.004 · Cited by 71 · see citing works
Detecting predatory behavior in game chats
Cheong, Y.-G., Jensen, A. K., Gudnadottir, E. R., Bae, B.-C., & Togelius, J. (2015) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2015.2424932 · Cited by 30 · see citing works
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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
Self-citationDetecting 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
Self-citation
- Authors
- Borj, P. R., Raja, K., & Bours, P.
- Venue
- 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA
- Publisher
- IEEE
- Published
- 2020-10-29
- Pages
- 1-6
- OpenAlex
- W3103958215
- Cited by
- 8
- References
- 3
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) (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 child sexual abuse, text preprocessing, feature extraction, bag-of-words, TF-IDF, GloVe embeddings
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