The rapid advancement of social networks and the convenience of internet
availability have accelerated the rampant spread of false news and rumors on
social media sites. Amid the COVID 19 epidemic, this misleading information has
aggravated the situation by putting peoples mental and physical lives in
danger. To limit the spread of such inaccuracies, identifying the fake news
from online platforms could be the first and foremost step. In this research,
the authors have conducted a comparative analysis by implementing five
transformer based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and
a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID 19
from the internet. COVID 19 Fake News Dataset has been used for training and
testing the models. Among all these models, the RoBERTa model has performed
better than other models by obtaining an F1 score of 0.98 in both real and fake
classes