An Optimized Deep Learning Model for Identifying Potentially Extremist and Radicalized Content

It can be concluded form the previous discussions that with the emergence of social media as a common platform for communication among different people and communities at large, the chances for malicious usage of social media platforms for malicious activities has also increased manifold. One such malicious activity is spreading radical content over social media platforms due to the ease of sharing among several individuals and groups. The challenging aspect though for social media agencies or security agencies is the screening of humongous amounts of data to detect radical content. With no clear boundary to demarcate radical and non-radical content, the classification problem becomes challenging as the data size increases. The proposed approach uses a Regression Learning  based Bayesian Regularized ANN. It has been shown that the proposed work attains much higher accuracy more than contemporary benchmark models.

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