Unsupervised Detection of Violent Content in Arabic Social Media

A monitoring system is proposed to detect violent content in Arabic social media. This is a new and challenging task due to the presence of various Arabic dialects in the social media and the non-violent context where violent words might be used. We proposed to use a probabilistic nonlinear dimensionality reduction technique called sparse Gaussian process latent variable model (SGPLVM) followed by k-means to separate violent from non-violent content. This framework does not require any labelled corpora for training. We show that violent and non-violent Arabic tweets are not separable using k-means in the original high dimensional space, however better results are achieved by clustering in low dimensional latent space of SGPLVM.