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A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models
2022 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2052038
ABSTRACT
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 people's 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. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 2022 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 2022 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2022 Year: 2022 Document Type: Article