Kim J., Liu Y., Lim W.Y., Thing V.L.L. (2017) An Empirical Study on Collective Online Behaviors of Extremist Supporters. In: Cong G., Peng WC., Zhang W., Li C., Sun A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science, vol 10604. Springer, Cham
Online social media platforms such as Twitter have been found to be misused by extremist groups, including Islamic State of Iraq and Syria (ISIS), who attract and recruit social media users. To prevent their influence from expanding in the online social media platforms, it is required to understand the online behaviors of these extremist group users and their followers, for predicting and identifying potential security threats. We present an empirical study about ISIS followers’ online behaviors on Twitter, proposing to classify their tweets in terms of political and subjectivity polarities. We first develop a supervised classification model for the polarity classification, based on natural language processing and clustering methods. We then develop a statistical analysis of term-polarity correlations, which leads us to successfully observe ISIS followers’ online behaviors, which are in line with the reports of experts.
This material is based on research work supported by the Singapore National Research Foundation under NCR Award No. NRF2014NCR-NCR001-034.