Determining Child Sexual Abuse Posts based on Artificial Intelligence

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dc.contributor.authorMcKeever, S., Thorpe, C., & Ngo, V.
dc.date.accessioned2023-05-15T16:10:32Z
dc.date.available2023-05-15T16:10:32Z
dc.date.issued2023
dc.description.abstractThe 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 ([1]). 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 ([2]). 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.en_US
dc.identifier.citationMcKeever, S., Thorpe, C., & Ngo, V. (2023). Determining Child Sexual Abuse Posts based on Artificial Intelligence. Technological University Dublin.en_US
dc.identifier.urihttps://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1415&context=scschcomcon
dc.identifier.urihttp://hdl.handle.net/11212/5838
dc.language.isoenen_US
dc.publisherTechnological University Dublinen_US
dc.subjectpolicyen_US
dc.subjectchild sexual abuse materialen_US
dc.subjectinterneten_US
dc.subjectmachine learningen_US
dc.subjectdark weben_US
dc.subjectresearchen_US
dc.subjectpost contenten_US
dc.titleDetermining Child Sexual Abuse Posts based on Artificial Intelligenceen_US
dc.typeArticleen_US

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