LAUSR

Nowadays the world of modern technologies brings new ways of communication and interaction between people. Online communication becomes faster and more convenient, but it also enables an exchange of more dangerous information. Antisocial behaviour in online web discussions becomes one of the most serious problems. This paper is focused on the analysis and identification of the most typical attributes of antisocial behaviour in the online space. Our research attempts to distinguish the most characteristic features of suspicious contributors to identify attributes that can define antisocial behaviour in the best way. The main objective is to evaluate the success of these attributes in the automatic detection of the suspicious contributors—trolls using classification methods of machine learning such as naïve Bayes, decision trees, random forest, logistic regression, and support vector machine. The methods were selected from the point of view of evaluation of suitability of individual attributes and evaluation of selected sets of attributes. The results of test of models learned using mentioned methods are discussed from this point of view and lead to a selection of a small set of attributes from all considered attributes.