How to take advantage of behavioral features for the early detection of grooming in online conversations

Author(s)

Milon-Flores, Daniela F. ; Cordeiro, Robson L. F.

• Document type: Journal article
• Source: KNOWLEDGE-BASED SYSTEMS; v. 240, p. 29-pg., 2022-01-22.

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 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. To the best of our knowledge, this is the first work to employ behavioral features for the early detection of grooming. Furthermore, 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 of the state-of-the-art methods when processing these new datasets. (C)& nbsp;2021 Elsevier B.V. All rights reserved.& nbsp; (AU)

• FAPESP's process: 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Regular Research Grants
• FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/05714-5 - Mining Frequent Data Streams of High Dimensionality with a Case Study in Digital Games
• Grantee: Robson Leonardo Ferreira Cordeiro
• Support Opportunities: Regular Research Grants