Detecting Extremism on Twitter During U.S. Capitol Riot Using Deep Learning Techniques

Abstract

In the 21st century, social media platforms have become famous for communicating ideas, opinions, and emotions. These platforms are influential in reaching out to youth, recruiting, and spreading propaganda. Extremist groups are now active users of social media platforms; therefore, it is necessary to monitor their activities. Therefore, there is an urgent need to detect extremism on social media platforms. Existing research on extremism lacks a dedicated extremism dataset and provides minimal insights into extremism texts. This study introduces the development of an extremism dataset containing tweets collected from Twitter and classifying extremism texts as propaganda, recruitment, radicalization, and non-extremism. The proposed extremism dataset is evaluated using different Artificial Intelligence approaches such as Bi-LSTM, BERT, RoBERTa, and DistilBERT. Among the four models, RoBERTa proved to be the most suitable for detecting extremism on social media, with an accuracy of 95%.