论文标题
我们可以自动对在线儿童性剥削话语分析吗？
Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
论文作者
论文摘要
社交媒体的日益普及引起了人们对儿童在线安全的关注。未成年人与具有掠夺性意图的成年人之间的互动是一个特别严重的关注点。在线性修饰的研究通常依靠领域专家手动注释对话，从而限制了规模和范围。在这项工作中，我们测试了良好的方法可以检测到对话行为并取代专家的注释者。通过在线修饰的心理学理论的启示，我们标记了$ 6772 $ $聊天消息，由儿童性犯罪者发送的聊天消息，其中一种是一种十一个掠夺性行为。我们训练字袋和自然语言推断模型来对每种行为进行分类，并表明，表现最好的模型以一致但不与人类注释相关的方式对行为进行分类。
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.
