  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
(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 best approach is based on
an ensemble of three transformer models (BERT, ALBERT, and XLNET) to 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 obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.
