Online Child Grooming Detection: Challenges and Future Directions

Abstract

In the era of ever-growing online social networking communities, reports of online crimes of various forms and targeting are growing exponentially, highlighting the imperative need for the development and enforcement of solutions and measures aimed at early detection and prevention. Specifically, in today’s landscape, child sexual abuse (CSA) and especially online grooming activities are even more prominent given the more intense involvement of young people in these communities. Grooming detection techniques, designed using machine learning, have been at the forefront of prevention and protection of minors. However, current approaches face significant challenges that affect their efficacy and usability. In this chapter, we investigate the challenges faced towards creating effective grooming detection systems and propose future directions to be explored as part of CESAGRAM project’s response to child sexual abuse.