Not So Cute but Fuzzy: Estimating Risk of Sexual Predation in Online Conversations

The sexual exploitation of minors is a known and persistent problem for law enforcement. Assistance in prioritizing cases of sexual exploitation of potentially risky conversations is crucial. While attempts to automatically triage conversations for the risk of sexual exploitation of minors have been attempted in the past, most computational models use features which are not representative of the grooming process that is used by investigators. Accurately annotating an offender corpus for use with machine learning algorithms is difficult because the stages of the grooming process feed into one another and are non-linear. In this paper we propose a method for labeling risk, tied to stages and themes of the grooming process, using fuzzy sets. We develop a neural network model that uses these fuzzy membership functions of each line in a chat as input and predict the risk of interaction.

I. Introduction

The sexual exploitation of online youths is a known and persistent problem. The National Center for Missing and Exploited Children (NCMEC) received 10.2 million reports of suspected child exploitation in 2017 [1]. Researchers have suggested the exploitation of minors online is also likely under-reported, due in part to the offender encouraging the minor to keep the interaction a secret and the minor acquiescing to the request [2]. Furthermore, handling the cases we even know about has placed additional strain on law enforcement agencies [3], and any help with prioritizing high risk conversations is a step forward.