Ravsimar Sodhi  received his MS Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Dr. Radhika Mamidi. Here’s a summary of his research work on Tackling Targeted Negative Speech on Social Media through Text Classification and Style Transfer:

Online abuse and offensive language on social media have become widespread problems in today’s digital age. With the growing use of the web in our daily lives, identifying and countering such speech has become a popular topic in the research community. Authorities of many countries worldwide recognise this as a serious problem, particularly because the reach of the internet has grown exponentially over the past years. Most previous work in this area has focused on detecting such harmful speech to prevent users from experiencing the adverse impact of the same. Detection is achieved chiefly through text classification models, which learn to recognise hate speech/offensive speech from neutral/standard sentences. More recently, with the advent of style transfer in text, many such negative attributes of speech are transferred into positive ones. One such instance is sentiment transfer, in which negative reviews are rewritten as positive ones while attempting to preserve the non-stylistic content of the input. In this thesis, we contribute the JDC (Jibe and Delight Corpus), a Reddit-based dataset consisting of 68, 159 insults and 51, 102 compliments. Our dataset is unique and differs from typical online abuse datasets since instead of being targeted at a community or race (in case of hate speech), they are targeted at individuals, and exhibit “creativity” in their formation, and so do not rely on profanity or slurs to convey the negative intent of the insult. We also perform extensive experiments on the same on two different fronts. We first work on the detection of such speech by analysing the results of various types of text classification models. Secondly, we propose to convert such insults into compliments by using unsupervised style transfer. We benchmark multiple existing state-of-the-art models for both text classification and unsupervised style transfer on the dataset. Our best classification model shows an accuracy of 97.8% and an F1 score of 0.972. We analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.