Identification of extremism on Twitter

Abstract

Identifying extremist-associated conversations on Twitter is an open problem. Extremist groups have been leveraging Twitter (1) to spread their message and (2) to gain recruits. In this paper, we investigate the problem of determining whether a particular Twitter user engages in extremist conversation. We explore different Twitter metrics as proxies for misbehavior, including the sentiment of the user's published tweets, the polarity of the user's ego-network, and user mentions. We compare different known classifiers using these different features on manually annotated tweets involving the ISIS extremist group and find that combining all these features leads to the highest accuracy for detecting extremism on Twitter.