Author ORCID Identifier

Document Type

Conference Paper


Computer Sciences, Information Science, Social issues

Publication Details

Accepted as an Oral Research Presentation. In the 2023 International Society for the Prevention of Child Abuse & Neglect Congress (ISPCAN-2023), Edinburgh, Scotland, UK, September 24-27, 2023


The volume of child sexual abuse materials (CSAM) created and shared daily both surface web platforms such as Twitter and dark web forums is very high. Based on volume, it is not viable for human experts to intercept or identify CSAM manually. However, automatically detecting and analysing child sexual abusive language in online text is challenging and time-intensive, mostly due to the variety of data formats and privacy constraints of hosting platforms. We propose a CSAM detection intelligence algorithm based on natural language processing and machine learning techniques. Our CSAM detection model is not only used to remove CSAM on online platforms, but can also help determine perpetrator behaviours, provide evidences, and extract new knowledge for hotlines, child agencies, education programs and policy makers.



the Safe Online Initiative of End Violence and the Tech Coalition

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.