Identification of markers and artificial intelligence-based classification of radical twitter data

Abstract

Social networks have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda, and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. We aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content. This article presents our experience and the results of collecting, analyzing, and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. We used artificial intelligence and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated. We report the classification accuracy of the K-Nearest Neighbor, Bernoulli Naïve Bayes, and Support Vector Machine (One-Against-All and All-Against-All) algorithms. We achieved a high classification F1 score of 83%. The work in this paper will hopefully aid more accurate classification of radical content.