A Bibliometric Analysis of Online Extremism Detection

The Internet has become an essential part of modern communication. People are sharing ideas, thoughts, and beliefs easily, using social media. This sharing of ideas has raised a big problem like the spread of the radicalized extremist ideas. The various extremist organizations use the social media as a propaganda tool. The extremist organizations actively radicalize and recruit youths by sharing inciting material on social media. Extremist organizations use social media to influence people to carry out lone-wolf attacks. Social media platforms employ various strategies to identify and remove the extremist content. But due to the sheer amount of data and loopholes in detection strategies, extremism remain undetected for a significant time. Thus, there is a need of accurate detection of extremism on social media. This study provides Bibliometric analysis and systematic mappings of existing literature for radicalisation or extremism detection. Bibliometric analysis of Machine Learning and Deep Learning articles in extremism detection are considered. This is performed using SCOPUS database, with the tools like Sciencescape and VOS Viewer. It is observed that the current literature on extremist detection is focused on a particular ideology. Though it is noted that few researchers are working in the extremism detection area, it is preferred among researchers in the recent years