Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
Date
2022
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Publisher
arXiv
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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Keywords
online safety, social media, child sexual exploitation, online grooming, manipulation, detection
Citation
Cook, D., Zilka, M., DeSandre, H., Giles, S., Weller, A., & Maskell, S. (2022). Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?. arXiv preprint arXiv:2209.12320.