Automatic Detection of Antisocial Behaviour in Texts

A considerable amount of effort has been made to reduce the physical manifestation of antisocial behaviour (ASB) in communities. However, the key to the early detection of ASB is, in many cases, in observing its manifestations in written language, which has not been studied in detail. In this work, we search for linguistic features that pertain to ASB in order to use those features for the automatic identification of ASB in texts. We use an ASB text corpus we have collected as a machine learning resource and approach the detection of ASB in texts as a binary classification problem where discriminating features are taken from the linguistic representation of texts in the form bag-of-words and ontology-based emotion descriptors. Results from preliminary experiments show that by exploiting the emotional information together with Bag-of-Words (BoW) over 90% accuracy in the classification of ASB in texts is reached. Our findings have positive implications in the early detection of potentially harmful behaviour.