"Our Little Secret": pinpointing potential predators

Abstract

The word "Paedophilia" has come a long way from its Greek origin of child-companionship to a Mental Disorder, Social Taboo and Criminal Offence. Various laws are in place to help control such behaviour, protect the vulnerable and restrain related criminal offences. However, enforcement of such laws has become a significant challenge with the advent of social media creating a new platform for this old crime. This move necessitates consideration of approaches that are suited to this new platform and the way in which it affects the Cycle of Entrapment. This paper reviews definitions of, and features of, paedophilia and other related -philias, and sexual offences against children, and seeks through the understanding of these to determine where specific detection approaches are effective. To this end, we present our own detection approach which is geared towards predatory behaviours, which can be a precursor to sexual offences against children, and which directly references this Cycle of Entrapment. Our approach has shown early promise with an F1 score of 0.66 for training data but only achieving 0.48 for testing data on a collection of chat logs of sexual predators. The results were later improved to achieve an F1 score of 0.77 for train and 0.54 for test data based on the approach.

Technical Mapping of the Grooming Anatomy Using Machine Learning Paradigms: An Information Security Approach

“…They include a list of features, which includes feelings and other characteristics based on the content. In their experimental results they describe that the classification based on their characteristics can discriminate pedophiles from non-pedophiles with great precision [13], [24], [25].…”

Detecting sexual predators in chats using behavioral features and imbalanced learning

“…Their main algorithm analyzes each chat and detects lexical patters for each of the aforementioned topics. In a subsequent research, the authors improved their categories by including a wider list of word patterns and by filtering conversations that were discussing computer topics (Vartapetiance and Gillam 2014). Moreover, they investigated the use of machine learning algorithms (decision trees, Naive Bayes, and SVM) for selecting the best attributes.…”