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.
翻译:社交媒体的日益普及引起了人们对儿童在线安全的担忧。 未成年人和成人之间带有掠夺性意图的互动是一个特别严重的问题。 在线性美容研究往往依靠域专家手动对谈话进行批注,限制规模和范围。 在这项工作中,我们测试如何使用良好的自动方法来检测谈话行为,并替换专家人类旁听员。 根据在线美容的心理理论,我们用11种掠夺性行为之一将儿童-性罪犯发送的6772美元聊天信息标注为6772美元。 我们训练了将每种行为分类的字包和自然语言推论模型,并展示了最优秀的表演模型将行为分类的方式与人文注释一致,但不是同时进行。