A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models
This work addresses the spread of misleading information during the COVID-19 pandemic, which poses risks to public health, but it is incremental as it applies existing methods to a specific dataset.
The authors tackled the problem of detecting COVID-19 fake news online by comparing five transformer-based models, finding that RoBERTa achieved the best performance with an F1 score of 0.98 for both real and fake classes.
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.