Modeling Grooming as a Conversational Phenomenon: The Importance of Turn Progression in Detecting Predatory Conversations
Abstract
Online grooming detection is commonly formulated as a text classification task, although predatory behavior emerges through a progressive conversational process involving trust building, emotional bonding, and gradual escalation. This paper investigates whether grooming is better characterized as a conversational modeling problem than as a conventional document classification task. Using the PAN12 Sexual Predator Identification benchmark, we systematically compare lexical, distributed, contextual, and long-context representations combined with supervised classifiers, in-context learning, pre-trained moderation models, and hierarchical conversational architectures. To explicitly model conversational progression, we introduce the Contextual Grooming Pattern Network (CGPN), a turn-level architecture that encodes conversational turns with MiniLM and models their temporal evolution through a bidirectional GRU with attention pooling. CGPN achieves the best overall performance, outperforming flat-concatenation approaches and demonstrating that preserving conversational structure provides complementary information beyond aggregated textual content. We further show that discriminative signals emerge within the first 25% of the dialogue and that effective detection depends less on explicit toxicity than on relational patterns such as affection, connection, isolation, information seeking, and gradual escalation. Across linguistic, structural, early-detection, and explainability analyses, the results provide converging evidence that grooming is fundamentally a progressive conversational phenomenon and that explicitly modeling turn dynamics is critical for its detection.
Keywords:
Online grooming detection, conversational modeling, text representation, contextual embeddings, explainable AI
References
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Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018. Interpretability Beyond Classification Output: Quantitative Testing with Concept Activation Vectors (TCAV). In International Conference on Machine Learning.
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Binny Mathew, Punyajoy Saha, Seid Muhie Yerdon, Tirthankar Sandhan, and Animesh Mukherjee. 2021. Hatexplain: A benchmark dataset for explainable hate speech detection. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 14867–14875.
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Tomáš Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems, Vol. 26.
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2021. Deep learning–based text classification: a comprehensive review. ACM computing surveys (CSUR) 54, 3 (2021), 1–40.
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Thanh Thi Nguyen, Campbell Wilson, and Janis Dalins. 2024. Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 613–618. DOI: 10.14428/esann/2024.ES2024-222
Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016. Abusive language detection in online user content. In Proceedings of the 25th international conference on world wide web. 145–153.
Rachel O’Connell, J. Price, and C. Barrow. 2004. Cyber stalking, abusive cyber sex and online grooming: A programmatic intervention. University of Central Lancashire, Centre for Cyber Victim Counselling (2004).
Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and Mei-Chun Hsu. 2001. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In Proceedings of the 17th International Conference on Data Engineering.
R Pelle, C Alcântara, and VP Moreira. 2018. A classifier ensemble for offensive text detection. In Proceedings of the 24th Brazilian Symposium on Multimedia and the Web (WebMedia). 237–243.
João Pires and Jorge Villar. 2012. Sexual Predator Identification in Chat Conversations. In CLEF (Online Working Notes/Labs/Workshop).
Flor Miriam Plaza-del Arco et al. 2021. Early detection of cyberbullying on social media networks. Future Generation Computer Systems 125 (2021), 706–717.
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing.
Kelly Reynolds, April Kontostathis, and Lynne Edwards. 2011. Using machine learning to detect cyberbullying. In 2011 10th International Conference on Machine Learning and Applications and Workshops. IEEE, 241–244.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why should i trust you?"Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135–1144.
Julian Risch and Ralf Krestel. 2020. Toxic comment detection in online discussions. Deep Learning-Based Approaches for Sentiment Analysis (2020), 85–109.
Joni Salminen et al. 2018. Anatomy of online hate: developing a taxonomy and machine learning models for identifying and classifying hate in online news media. ICWSM (2018).
Anna Schmidt and Michael Wiegand. 2017. A survey on hate speech detection using natural language processing. Proceedings of the 5th International Workshop on Natural Language Processing for Social Media (2017), 1–10.
M Schulze et al. 2020. Online grooming detection with advanced machine learning architectures. Expert Systems with Applications (2020).
Peter K. Smith and Georges Steffgen. 2020. Cyberbullying Through the New Media: Findings from an International Network. Psychology Press (2020).
Felipe Viegas, Sérgio Canuto, Christian Gomes, Washington Cunha, Thierson Rosa, Sabir Ribas, Leonardo Rocha, and Marcos André Gonçalves. 2019. CluWords: Exploiting Semantic Word Clustering Representation for Enhanced Topic Modeling. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, 753–761. DOI: 10.1145/3289600.3291032
Felipe Viegas, Washington Cunha, Christian Gomes, Antônio Pereira, Leonardo Rocha, and Marcos Goncalves. 2020. CluHTM: Semantic Hierarchical Topic Modeling Based on CluWords. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 8138–8150. DOI: 10.18653/v1/2020.acl-main.724
Felipe Viegas, Washington Cunha, Christian Gomes, Leonardo Rocha, and Marcos Goncalves. 2025. Exploiting Contextual Embeddings in Hierarchical Topic Modeling and Investigating the Limits of the Current Evaluation Metrics. Computational Linguistics (2025).
Inna Vogel et al. 2020. Detecting online grooming using deep learning. Neurocomputing 404 (2020), 310–321.
Hamed Waezi, Reza Barzegar, and Hossein Fani. 2024. Osprey: A Reference Framework for Online Grooming Detection via Neural Models and Conversation Features. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 4101–4106. DOI: 10.1145/3627673.3679974
Zeerak Waseem and Dirk Hovy. 2016. Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of the NAACL student research workshop. 88–93.
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. 1480–1489.
Dawei Yin et al. 2009. Detection of harassment on web 2.0. In Proceedings of the Content Analysis in the WEB 2.0. 1–7.
Manzil Zaheer, Guru Guruganesh, Kumar Avinash Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. 2020. Big bird: Transformers for longer sequences. In Advances in Neural Information Processing Systems, Vol. 33. 17283–17297.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Long-former: The Long-Document Transformer. In arXiv preprint arXiv:2004.05150.
Roxane Bois et al. 2023. Early Detection of Online Grooming with Language Models. Medium Engineering Blog (2023).
Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski. 2018. A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks 106 (2018), 249–259.
Pete Burnap and Matthew L Williams. 2015. Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making. Policy & Internet 7, 2 (2015), 223–242.
Despoina Chatzakou et al. 2017. Mean birds: Detecting aggression and bullying on twitter. In Proceedings of the 2017 ACM on web science conference. 13–22.
Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794.
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).
Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2023. An Effective, Efficient, and Scalable Confidence-Based Instance Selection Framework for Transformer-Based Text Classification. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 665–674. DOI: 10.1145/3539618.3591638
Washington Cunha, Felipe Viegas, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2025. A Noise-Oriented and Redundancy-Aware Instance Selection Framework. ACM Transactions on Information Systems 43, 1 (2025). DOI: 10.1145/3701769
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th International AAAI Conference on Web and Social Media.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019.
Karthik Dinakar, Roi Reichart, and Henry Lieberman. 2011. Modeling the detection of text-based cyberbullying. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5.
David Finkelhor and Lisa M Jones. 2006. Youth exposure to violence: Past, current, and future trends. The Journal of Pediatrics 149, 1 (2006), S116–S119.
Paula Fortuna and Sérgio Nunes. 2018. A survey on automatic corpus-based machine learning for hate speech detection. ACM Computing Surveys (CSUR) 51, 4 (2018), 1–29.
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2020.
Victor Machado Gonzaga, Nils Murrugarra-Llerena, and Ricardo Marcacini. 2021. Multimodal intent classification with incomplete modalities using text embedding propagation. In Proceedings of the 27th Brazillian Symposium on Multimedia and the Web (WebMedia). 1–8.
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux. 2022. Why do tree-based models still outperform deep learning on typical tabular data?. In Advances in Neural Information Processing Systems, Vol. 35. 507–520.
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. 2023. TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. In International Conference on Learning Representations.
Justin M Johnson and Taghi M Khoshgoftaar. 2019. Survey on deep learning with class imbalance. Journal of Big Data 6, 1 (2019), 1–54.
Erin Jones and Sarah Thompson. 2018. Identifying Online Predators Using Machine Learning. In Proceedings of the 2018 ACM Workshop on Privacy in the Electronic Society.
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems, Vol. 30.
A Khalil et al. 2021. Early detection of online grooming using natural language processing. Computer Speech & Language 68 (2021), 101188.
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018. Interpretability Beyond Classification Output: Quantitative Testing with Concept Activation Vectors (TCAV). In International Conference on Machine Learning.
Dohyeon Kim, Taehoon Kim, and Jihoon Yang. 2025. Early Detection of Online Grooming with Language Models. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing. 963–970. DOI: 10.1145/3672608.3707796
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020. Concept Bottleneck Models. In Proceedings of the 37th International Conference on Machine Learning (ICML). 5338–5348.
Scott M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30 (2017).
Sean MacAvaney et al. 2019. Hate speech detection: Challenges and solutions. PLoS one 14, 8 (2019), e0221152.
Binny Mathew, Punyajoy Saha, Seid Muhie Yerdon, Tirthankar Sandhan, and Animesh Mukherjee. 2021. Hatexplain: A benchmark dataset for explainable hate speech detection. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 14867–14875.
J McGhee et al. 2011. Risk factors for online grooming. Journal of Forensic Psychology (2011).
Tomáš Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems, Vol. 26.
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2021. Deep learning–based text classification: a comprehensive review. ACM computing surveys (CSUR) 54, 3 (2021), 1–40.
Gabriel Nascimento, Flavio Carvalho, Alexandre M da Cunha, Carlos Roberto Viana, and Gustavo P Guedes. 2019. Hate Speech Detection Using Brazilian Imageboards. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web (WebMedia). 325–328.
Thanh Thi Nguyen, Campbell Wilson, and Janis Dalins. 2024. Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 613–618. DOI: 10.14428/esann/2024.ES2024-222
Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016. Abusive language detection in online user content. In Proceedings of the 25th international conference on world wide web. 145–153.
Rachel O’Connell, J. Price, and C. Barrow. 2004. Cyber stalking, abusive cyber sex and online grooming: A programmatic intervention. University of Central Lancashire, Centre for Cyber Victim Counselling (2004).
Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and Mei-Chun Hsu. 2001. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In Proceedings of the 17th International Conference on Data Engineering.
R Pelle, C Alcântara, and VP Moreira. 2018. A classifier ensemble for offensive text detection. In Proceedings of the 24th Brazilian Symposium on Multimedia and the Web (WebMedia). 237–243.
João Pires and Jorge Villar. 2012. Sexual Predator Identification in Chat Conversations. In CLEF (Online Working Notes/Labs/Workshop).
Flor Miriam Plaza-del Arco et al. 2021. Early detection of cyberbullying on social media networks. Future Generation Computer Systems 125 (2021), 706–717.
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing.
Kelly Reynolds, April Kontostathis, and Lynne Edwards. 2011. Using machine learning to detect cyberbullying. In 2011 10th International Conference on Machine Learning and Applications and Workshops. IEEE, 241–244.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why should i trust you?"Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135–1144.
Julian Risch and Ralf Krestel. 2020. Toxic comment detection in online discussions. Deep Learning-Based Approaches for Sentiment Analysis (2020), 85–109.
Joni Salminen et al. 2018. Anatomy of online hate: developing a taxonomy and machine learning models for identifying and classifying hate in online news media. ICWSM (2018).
Anna Schmidt and Michael Wiegand. 2017. A survey on hate speech detection using natural language processing. Proceedings of the 5th International Workshop on Natural Language Processing for Social Media (2017), 1–10.
M Schulze et al. 2020. Online grooming detection with advanced machine learning architectures. Expert Systems with Applications (2020).
Peter K. Smith and Georges Steffgen. 2020. Cyberbullying Through the New Media: Findings from an International Network. Psychology Press (2020).
Felipe Viegas, Sérgio Canuto, Christian Gomes, Washington Cunha, Thierson Rosa, Sabir Ribas, Leonardo Rocha, and Marcos André Gonçalves. 2019. CluWords: Exploiting Semantic Word Clustering Representation for Enhanced Topic Modeling. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, 753–761. DOI: 10.1145/3289600.3291032
Felipe Viegas, Washington Cunha, Christian Gomes, Antônio Pereira, Leonardo Rocha, and Marcos Goncalves. 2020. CluHTM: Semantic Hierarchical Topic Modeling Based on CluWords. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 8138–8150. DOI: 10.18653/v1/2020.acl-main.724
Felipe Viegas, Washington Cunha, Christian Gomes, Leonardo Rocha, and Marcos Goncalves. 2025. Exploiting Contextual Embeddings in Hierarchical Topic Modeling and Investigating the Limits of the Current Evaluation Metrics. Computational Linguistics (2025).
Inna Vogel et al. 2020. Detecting online grooming using deep learning. Neurocomputing 404 (2020), 310–321.
Hamed Waezi, Reza Barzegar, and Hossein Fani. 2024. Osprey: A Reference Framework for Online Grooming Detection via Neural Models and Conversation Features. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 4101–4106. DOI: 10.1145/3627673.3679974
Zeerak Waseem and Dirk Hovy. 2016. Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of the NAACL student research workshop. 88–93.
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. 1480–1489.
Dawei Yin et al. 2009. Detection of harassment on web 2.0. In Proceedings of the Content Analysis in the WEB 2.0. 1–7.
Manzil Zaheer, Guru Guruganesh, Kumar Avinash Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. 2020. Big bird: Transformers for longer sequences. In Advances in Neural Information Processing Systems, Vol. 33. 17283–17297.
Published
2026-10-27
How to Cite
CAMPOS, João Marcos Tomáz S.; CHAVES, Diogo T.; GOMES, Rafael M.; MEIRA JR., Wagner; GONÇALVES, Marcos André; ROCHA, Leonardo.
Modeling Grooming as a Conversational Phenomenon: The Importance of Turn Progression in Detecting Predatory Conversations. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 32. , 2026, Lavras/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 328-337.
DOI: https://doi.org/10.5753/webmedia.2026.30722.
