Machine learning to detect online grooming

Abstract: Online grooming is a major problem in today's society where more and more time is spent online. To become friends and establish a relationship with their young victims in online communities, groomers often pretend to be children. In this paper we describe an approach that can be used to detect if an adult is pretending to be a child in a chat room conversation. The approach involves a two step process wherein authors are first classified as being a children or adults, and then each child is being examined and false children distinguished from genuine children. Our results shows that even if it is hard to separate ordinary adults from children in chat logs it is possible to distinguish real children from adults pretending to be children with a high accuracy. In this report the accuracy of the methods proposed is discussed, as well as the features that were important in their success. We believe that this work is an important step towards automated analysis of chat room conversation to detect possible attempts of grooming. Our approach where we use text analysis to distinguish adults who are pretending to be children from actual children could be used to inform children about the true age of the person that they are communicating. This would be a step towards making the Internet more secure for young children and eliminate grooming.