Author ORCID Identifier
Document Type
Conference Paper
Disciplines
Computer Sciences, Information Science, Social issues
Abstract
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.
DOI
https://doi.org/10.21427/S3GQ-3536
Recommended Citation
Susan Mckeever, Christina Thorpe and Vuong M. Ngo. 2023. Determining Child Sexual Abuse Posts based on Artificial Intelligence. In the 2023 International Society for the Prevention of Child Abuse & Neglect Congress (ISPCAN-2023), Edinburgh, Scotland, UK, September 24-27, 2023, DOI: 10.21427/S3GQ-3536
Funder
the Safe Online Initiative of End Violence and the Tech Coalition
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Other Social and Behavioral Sciences Commons
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