Multi-Ideology Multi-Class Extremism Classification Using Deep Learning Techniques

Social media is an integral part of today’s social communication. Social media platforms have a global reach with immense popularity among the young generation. This reach and influencing power of social media has attracted extremist and terrorist organizations to social media platforms. Numerous terrorist organizations like ISIS, Taliban, Al-Qaeda, and Proud Boys and conspiracy theory groups like Alt-Right and QAnon spread their propaganda, radicalize and recruit youths via social media platforms. Thus, online extremism research is imperative to monitor extremists’ influence and their spread of hate on social media. The existing research is limited to the specific ideology, which results in a bias towards a particular ideology. The classification of extremism is presented only in binary or tertiary classes with no further insights. This research work presents the development of a seed dataset and balanced multi-ideology extremism text dataset with multi-class labels. Recently natural language processing with deep learning has gained significant attention in extremism detection research. Thus this research focuses on collecting, cleaning and classifying the extremist tweets. This study presents a multi-class classification of the balanced multi-ideology dataset. This dataset is termed Merged Islamic State of Iraq and Syria (ISIS) /Jihadist-White Supremacist (MIWS). The MIWS dataset is evaluated using pre-trained Bidirectional Encoder Representation for Transformers (BERT) and variants like Robustly Optimized BERT Pretraining Approach (RoBERTa) and DistilBERT and achieves the highest f1-score of 0.72. RoBERTa and DistilBERT provide f1-score of 0.68 and 0.71, respectively. Thus, deep learning can be effectively used to identify extremism from multiple ideologies and segregate them into propaganda, radicalization and recruitment.