The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.
How to take advantage of behavioral features for the early detection of grooming in online conversations
Abstract
Detecting grooming behavior in online conversations has become a growing problem due to the large number of messaging platforms that children and young people use nowadays. The biggest drawback is the lack of tools focused on the automatic prevention of this risk. This paper proposes seven Behavioral Features (BFs) to be used for early grooming detection. A detailed study is conducted to understand the background that allows these features to contribute to tasks of early classification. Besides, we introduce the Behavioral Feature - Profile Specific Representation (BF-PSR) framework as an extension of the well-known Profile Specific Representation (PSR) framework to properly employ the proposed behavioral features. Experimental results reveal that our proposal outperforms all the concurrent methods and obtains state-of-the-art performance in the area of early grooming detection. Specifically, the new BF-PSR framework achieves a gain of more than 40% in effectiveness over five competitors when only 10% of the SGD conversations content is available, thus it shows a substantial advantage to allow the early detection of grooming; besides, it maintains a similar gain in effectiveness as more data arrives. Furthermore, to the best of our knowledge, this is the first work to employ behavioral features for the early detection of grooming. On the other hand, we have assembled two new datasets called PJZ and PJZC to mitigate the lack of data in the grooming detection area. Both sets are publicly available for download aimed at fostering further researches. Additional experiments reveal that our BF-PSR framework outperforms all state-of-the-art methods when processing these new datasets.
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Most prior work has focused on detecting perpetrators' behaviors, leaving a limited understanding of how teens might respond to unwanted advances.
“Yet, most prior work has focused on detecting perpetrators' behaviors, leaving a limited understanding of how teens might respond to such unwanted advances.”
From From Vulnerable to Resilient: Examining Parent and Teen Perceptions on How to Respond to Unwanted Cybergrooming Advances (2026) · cites this paper as [59]
Most computer science literature focuses on detecting perpetrators' behaviors using machine learning and natural language processing techniques.
“To combat cybergrooming, researchers have developed numerous computational approaches, with most computer science literature focuses on detecting perpetrators' behaviors using machine learning (ML) and natural language processing (NLP) techniques (e.g., [5, 16-19, 3234, 42, 44, 48, 59, 62, 72, 76-78]).”
From From Vulnerable to Resilient: Examining Parent and Teen Perceptions on How to Respond to Unwanted Cybergrooming Advances (2026) · cites this paper as [59]
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Metadata
- Authors
- Milon-Flores, D. F., & Cordeiro, R. L. F.
- Affiliations
- Universidade de São Paulo (BR)
- Corresponding author
- Cordeiro, Robson L.F.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 240
- Pages
- 108017
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4200478206
- Cited by
- 29
- References
- 34
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 14
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- behavioral characteristics, early detection, online discourse, child online safety, chatbot conversations, limited training data, datasets
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