A Review on Data Driven Approaches for Identifying Potentially Radical Content on Social Media Platforms

With advancements in technology and cyber warfare, terrorist and radical content is being used as a tools to spread violence and social unrest. Due to the complexity and the largeness of the data size, it is humanly infeasible to analyze the data manually or use statistical techniques. Thus machine learning based approaches are indispensable for the detection and classification of the radical and possible terrorist activity based content. In this approach, the use of social media messages in the form of tweets and messages have been considered. The paper presents the need for radical content filtration along with the basic models based on opinion mining for identifying potentially radical content. A literature review and theoretical background for identifying radical content in presented in this paper.