The missing resources for child online risk detection

Children’s daily use of the Internet exposes them to various online harms, which constitutes any online material or interactions that negatively impacts a child's well-being. Parental controls and monitoring are often insufficient to mitigate these risks, and children without parental support are especially vulnerable. Consequently, there is motivation for developing automatic systems capable of detecting these risks. To create effective detection mechanisms requires suitable datasets, both to train machine learning systems and to evaluate their real-world effectiveness. To that end, this paper employs a narrative review methodology to examine the availability and suitability of resources across four types of online risks posed to children: online grooming, cyberbullying, mental health, and extremism, radicalisation, and hate speech. Importantly, this paper highlights a stark lack of data pertaining to children for these detection tasks, identifying a clear research gap. Additionally, we highlight important considerations for determining whether a resource is suitable for the detection task at hand and outline the limitations of currently available resources. By highlighting the deficiencies in both the availability and suitability of resources, our paper calls for further research and development to bridge these gaps, offering actionable steps to advance the field and to ultimately ensure children’s safety online.