Detecting Sexually Predatory Behavior on Open-Access Online Forums

Singla, Yash

doi:10.1007/978-981-16-1543-6_3

Cited by 1 publication

(2 citation statements)

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“…In [46], an approach was presented to address class imbalance using hybrid sampling and class re-distribution to build an augmented dataset, and then classify it by using histogram gradient boosted decision trees (HGBDT). In [49], the authors also used a two-stage classifier whereby the chat logs were classified in the first stage using different classifiers that labelled the chats as non-predatory and predatory. In the second stage, false positive events were minimized.…”

Section: Ai Protection Mechanisms Against Child Predatorsmentioning

confidence: 99%

“…The most widely used technique for detecting child predators on chatting platforms is text classification, which utilizes the two available data sources reported in [53], [54]. ML methods used to detect cyberbullying [27], [28], [31], [48] and sexual predatory behavior [25], [29], [30], [32]- [40], [42]- [47], [49]- [52] remain the main focus of research over the last 10 years. In [55], a brief survey of ML algorithms was used to detect child grooming behavior on social media between 2007 and 2016.…”

Section: Introductionmentioning

confidence: 99%

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Child Safety and Protection in the Online Gaming Ecosystem

et al. 2022

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Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. Although the online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. There is a vital need to develop laws and policies to regulate and build minimum standards for the industry to safeguard and protect children online on the one hand, while promoting innovations in the gaming industry to preempt such threats. Many tools have been adapted to control threats against children in the form of content filtering and parental controls, thereby restricting contact with children to protect them from child predators. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. In this paper, we survey online threats to children in the gaming environment and present the limitations of existing solutions that address these threats. We also aimed to present the challenges that ML techniques face in protecting children against predatory behavior by presenting a systematic review of the available techniques in the literature. Therefore, this analysis provides not only recommendations to stakeholders to develop policies and practices that safeguard children when gaming, but also to the gaming industry to continue providing appropriate measures for a safe and entertaining gaming environment.

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Section: Ai Protection Mechanisms Against Child Predatorsmentioning

confidence: 99%