Abstract
Misinformation poses a significant challenge in the digital age, requiring robust methods to detect fake news. This study investigates the effectiveness of using Back Translation (BT) augmentation, specifically transformer-based models, to improve fake news detection in Romanian. Using a data set extracted from Factual.ro, the research finds that BT-augmented models show better accuracy, precision, recall, F1 score, and AUC compared to those using the original data set. Additionally, using mBART for BT augmentation with French as a target language improved the model’s performance compared to Google Translate. The Extra Trees Classifier and the Random Forest Classifier performed the best among the models tested. The findings suggest that the use of BT augmentation with transformer-based models, such as mBART, has the potential to enhance fake news detection. More research is needed to evaluate the effects in other languages.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
