# NoFake at CheckThat! 2021: Fake News Detection Using BERT

arXiv: https://arxiv.org/abs/2108.05419 · HTML: https://mlsanity.com/paper/2108.05419/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/1812.00315/) (2018)
- [AMUSED: An Annotation Framework of Multi-modal Social Media Data](https://mlsanity.com/paper/2010.00502/) (2020)

## Built on by (most cited first, 3 of 3)

- [Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News](https://mlsanity.com/paper/2109.12987/) (2021)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [Fake News Detection Using Majority Voting Technique](https://mlsanity.com/paper/2203.09936/) (2022)

## Abstract (arXiv)

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.