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Social media's growing popularity raises concerns around children's onlinesafety. Interactions between minors and adults with predatory intentions is aparticularly grave concern. Research into online sexual grooming has oftenrelied on domain experts to manually annotate conversations, limiting bothscale and scope. In this work, we test how well-automated methods can detectconversational behaviors and replace an expert human annotator. Informed bypsychological theories of online grooming, we label $6772$ chat messages sentby child-sex offenders with one of eleven predatory behaviors. We trainbag-of-words and natural language inference models to classify each behavior,and show that the best performing models classify behaviors in a manner that isconsistent, but not on-par, with human annotation.

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