Flexible inference for cyberbully incident detection

Abstract

We study detection of cyberbully incidents in online social networks, focusing on session level analysis. We propose several variants of a customized convolutional neural networks (CNN) approach, which processes users’ comments largely independently in the front-end layers, but while also accounting for possible conversational patterns. The front-end layer’s outputs are then combined by one of our designed output layers – namely by either a max layer or by a novel sorting layer, proposed here. Our CNN models outperform existing baselines and are able to achieve classification accuracy of up to 84.29% for cyberbullying and 83.08% for cyberaggression.