Automatic identification of predatory conversations in chat logs helps the law enforcement agencies act proactively through early detection of predatory acts in cyberspace. In this paper, we describe the novel application of a deep learning method to the automatic identification of predatory chat conversations in large volumes of chat logs. We present a classifier based on Convolutional Neural Network (CNN) to address this problem domain. The proposed CNN architecture outperforms other classification techniques that are common in this domain including Support Vector Machine (SVM) and regular Neural Network (NN) in terms of classification performance, which is measured by F 1 -score. In addition, our experiments show that using existing pre-trained word vectors are not suitable for this specific domain. Furthermore, since the learning algorithm runs in a massively parallel environment (i.e., general-purpose GPU), the approach can benefit a large number of computation units (neurons) compared to when CPU is used. To the best of our knowledge, this is the first time that CNNs are adapted and applied to this application domain.
['Mohammad Reza Ebrahimi', 'Ching Y. Suen', 'Olga Ormandjieva']
Detecting predatory conversations in social media by deep Convolutional Neural Networks