Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?

• URL: http://arxiv.org/abs/2209.12320v1
• Date: Sun, 25 Sep 2022 21:18:50 GMT
• Title: Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
• Authors: Darren Cook, Miri Zilka, Heidi DeSandre, Susan Giles, Adrian Weller, Simon Maskell
• Abstract summary: Social media's growing popularity raises concerns around children's online safety. Research into online sexual grooming has often relied on domain experts to manually annotate conversations. We test how well-automated methods can detect conversational behaviors and replace an expert human annotator.
• Score: 18.20420363291303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media's growing popularity raises concerns around children's online safety. Interactions between minors and adults with predatory intentions is a particularly grave concern. Research into online sexual grooming has often relied on domain experts to manually annotate conversations, limiting both scale and scope. In this work, we test how well-automated methods can detect conversational behaviors and replace an expert human annotator. Informed by psychological theories of online grooming, we label $6772$ chat messages sent by child-sex offenders with one of eleven predatory behaviors. We train bag-of-words and natural language inference models to classify each behavior, and show that the best performing models classify behaviors in a manner that is consistent, but not on-par, with human annotation.

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