Enhanced online grooming detection employing context determination and message-level analysis

Street, J ORCID: https://orcid.org/0000-0002-9305-8468, Ihianle, IK ORCID: https://orcid.org/0000-0001-7445-8573, Olajide, F and Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, 2025. Enhanced online grooming detection employing context determination and message-level analysis. Intelligent Systems with Applications, 28: 200607. ISSN 2667-3053

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Abstract

Online Grooming (OG) is a prevalent threat facing predominately children online, with groomers using deceptive methods to prey on the vulnerability of children on social media/messaging platforms. These attacks can have severe psychological and physical impacts, including a tendency towards revictimization. Current technical measures are inadequate, especially with the advent of end-to-end encryption which hampers message monitoring. Existing solutions focus on the signature analysis of child abuse media, which does not effectively address real-time OG detection. This paper proposes that OG attacks are complex, requiring the identification of specific communication patterns between adults and children alongside identifying other insights (e.g. Sexual language) to make an accurate determination. It introduces a novel approach leveraging advanced models such as BERT and RoBERTa for Message-Level Analysis and a Context Determination approach for classifying actor interactions, between adults attempting to groom children and honeypot children actors. This approach included the introduction of Actor Significance Thresholds and Message Significance Thresholds to make these determinations. The proposed method aims to enhance accuracy and robustness in detecting OG by considering the dynamic and multi-faceted nature of these attacks. Cross-dataset experiments evaluate the robustness and versatility of our approach. This paper’s contributions include improved detection methodologies and the potential for application in various scenarios, addressing gaps in current literature and practices.

Item Type:	Journal article
Publication Title:	Intelligent Systems with Applications
Creators:	Street, J., Ihianle, I.K., Olajide, F. and Lotfi, A.
Publisher:	Elsevier BV
Date:	December 2025
Volume:	28
ISSN:	2667-3053
Identifiers:	Number Type 10.1016/j.iswa.2025.200607 DOI 2529811 Other
Rights:	© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	19 Nov 2025 09:53
Last Modified:	19 Nov 2025 09:53
URI:	https://irep.ntu.ac.uk/id/eprint/54770

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