  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.
