Recent rapid technological advancements in online social networks such as Twitter have led to a great incline in spreading false information and fake news. Misinformation is especially prevalent in the ongoing coronavirus disease 2019 (COVID-19) pandemic, leading to individuals accepting bogus and potentially deleterious claims and articles. Quick detection of fake news can reduce the spread of panic and confusion among the public. For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic. Our approach is based on an ensemble of three transformer models (BERT, ALBERT, and XLNET) for detecting fake news. This model was trained and evaluated in the context of the ConstraintAI 2021 shared task "COVID19 Fake News Detection in English". Our system got 98.23 on the f1-score and ranked 5th among 110 teams.
Note: Code added for individual BERT model. Very soon, I will push the full code to this repo.