NoFake at CheckThat! 2021: Fake News Detection Using BERT
This work addresses the problem of inconsistent labeling in fake news detection for fact-checkers, but it is incremental as it applies an existing method (BERT) to new data.
The paper tackled fake news detection by developing a BERT-based model to predict domain and classification, achieving macro F1 scores of 83.76% for Task 3A and 85.55% for Task 3B using additional training data from fact-checked articles.
Much research has been done for debunking and analysing fake news. Many researchers study fake news detection in the last year, but many are limited to social media data. Currently, multiples fact-checkers are publishing their results in various formats. Also, multiple fact-checkers use different labels for the fake news, making it difficult to make a generalisable classifier. With the merge classes, the performance of the machine model can be enhanced. This domain categorisation will help group the article, which will help save the manual effort in assigning the claim verification. In this paper, we have presented BERT based classification model to predict the domain and classification. We have also used additional data from fact-checked articles. We have achieved a macro F1 score of 83.76 % for Task 3Aand 85.55 % for Task 3B using the additional training data.