Online Public Shaming Approach using Deep Learning Techniques


Mehdi Surani, Ramchandra Mangrulkar
Department of Computer Engineering, D.J.Sanghvi College of Engineering, Mumbai, India.


Public shaming on social media platforms like Twitter / Instagram / Facebook etc. have recently increased from the past years. This results in affecting an individual’s social, political, mental and financial life. The impact can range from mild bullying to severe depression. With the growing leniency on these social platforms, many people have started misusing the opportunity by turning to online bullying and hate speech. When something is posted online, it stays there forever and it becomes extremely hard taking something out of the digital world. Manually locating and categorizing such comments is a lengthy procedure and just cannot be relied upon. To solve this challenge, automation was performed to identify and classify the shamers. This has been done using the classic SVM model which worked on a given quantity of data. To identify the negative content being posted and discussed online, this paper further explores the deep learning system which can successfully classify these content pieces into proper labels. The text-based Convolution Neural Network (CNN) is the proposed model in this paper for this analysis.