RadScore: An Automated Technique to Measure Radicalness Score of Online Social Media Users

Social media platforms provide effective mediums for expressing opinions and thoughts on several topics openly. This does protect our right to freedom of speech, however the enormous reach of social media makes it a potential tool for widespread radicalization among the youth, irrespective of the geographical and demographical boundaries. This necessitates the need to effectively identify the content which is a source of mass online radicalization. In order to curb the propagation, security agencies need an automatic radicalization detection mechanism for mining the huge volumes of social media content. In this article, we propose an approach for detecting online radicalized accounts and quantifying the degree to which these user accounts are propagating radical content. We propose to use three novel features, i.e., Similarity to domain, presence of radical content and sentiment to calculate the radicalness score for each online user. Our algorithm uses a CNN-LSTM-based technique to effectively differentiate between radical/non-radical content with an accuracy of 93%. Our empirical results show that radicalness scores for known radicalized websites are higher as compared to the non-radical users. We believe this is a first ever attempt at quantifying the level of radicalization of users using scientific methods which can be very helpful to national security agencies in tracking suspicious online users and stop the spread of anti-national content on social media.