Paper Title
A SURVEY ON COVID-19 RELATED FAKE NEWS DETECTION USING MACHINE LEARNING MODELS

Abstract
Uncensored data explosion on social media platforms has on the one hand impelled fast and easy dissemination of news and facts, but at the same time poses serious threats because of its highly unreliable nature. Misinformation and disinformation are mainly prevalent at the time some important event is happening that people are curious about e.g. elections or something untoward happens like the COVID-19 pandemic. Because of the unprecedented nature of these events, people are susceptible to these bogus and potentially hazardous claims and articles. Therefore, we need an early detection mechanism to stop the spread of intentionally and unintentionally written fake news or claims. Past research has suggested various models based on machine learning, deep learning and pretrained language models to detect false news over the years. This research piece will try to assess the effectiveness of various relevant methods on the task of detecting fake news and false claims related to COVID-19 pandemic in this research. We will be using the combined corpus of two largest datasets available. We explore various pertained language models in addition to deep learning and conventional machine learning approaches and compare their performance. We find that RoBERTa in particular and Bert-based models in general outperform all other models. We believe this piece of research will help the research community a lot in exploring the said domain further. Keywords - Fake News Detection, Misinformation , COVID-19, Machine Learning, Language Models;