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Social Media has influenced the way people socially connect, interact andopinionize. The growth in technology has enhanced communication anddissemination of information. Unfortunately,many terror groups like jihadistcommunities have started consolidating a virtual community online for variouspurposes such as recruitment, online donations, targeting youth online andspread of extremist ideologies. Everyday a large number of articles, tweets,posts, posters, blogs, comments, views and news are posted online without acheck which in turn imposes a threat to the security of any nation. However,different agencies are working on getting down this radical content fromvarious online social media platforms. The aim of our paper is to utilise deeplearning algorithm in detection of radicalization contrary to the existingworks based on machine learning algorithms. An LSTM based feed forward neuralnetwork is employed to detect radical content. We collected total 61601 recordsfrom various online sources constituting news, articles and blogs. Theserecords are annotated by domain experts into three categories: Radical(R),Non-Radical (NR) and Irrelevant(I) which are further applied to LSTM basednetwork to classify radical content. A precision of 85.9% has been achievedwith the proposed approach

Detecting Radical Text over Online Media using Deep Learning