# Evaluating Deep Learning Approaches for Covid19 Fake News Detection

arXiv: https://arxiv.org/abs/2101.04012 · HTML: https://mlsanity.com/paper/2101.04012/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)

## Built on by (most cited first, 16 of 16)

- [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021)
- [COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning](https://mlsanity.com/paper/2101.06278/) (2021)
- [Multimodal Fake News Detection](https://mlsanity.com/paper/2112.04831/) (2021)
- [Comparative Study of Long Document Classification](https://mlsanity.com/paper/2111.00702/) (2021)
- [Hierarchical Neural Network Approaches for Long Document Classification](https://mlsanity.com/paper/2201.06774/) (2022)
- [Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation](https://mlsanity.com/paper/2304.10249/) (2023)
- [Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches](https://mlsanity.com/paper/2110.09338/) (2021)
- [Indonesia's Fake News Detection using Transformer Network](https://mlsanity.com/paper/2107.06796/) (2021)
- [On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations](https://mlsanity.com/paper/2201.00318/) (2022)
- [A Proposed Bi-LSTM Method to Fake News Detection](https://mlsanity.com/paper/2206.13982/) (2022)
- [A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models](https://mlsanity.com/paper/2208.01355/) (2022)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [Towards Simple and Efficient Task-Adaptive Pre-training for Text Classification](https://mlsanity.com/paper/2209.12943/) (2022)
- [X-CapsNet For Fake News Detection](https://mlsanity.com/paper/2307.12332/) (2023)
- [Automated Evidence Collection for Fake News Detection](https://mlsanity.com/paper/2112.06507/) (2021)
- [BharatGather: A Culturally-Informed Benchmark Dataset for Misinformation and Fake News Detection in Indian Public Events](https://mlsanity.com/paper/2609.02895/) (2026)

## Abstract (arXiv)

Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin dictates an alarming story. These platforms have led to an increase in the creation and spread of fake news. The fake news has not only influenced people in the wrong direction but also claimed human lives. During these critical times of the Covid19 pandemic, it is easy to mislead people and make them believe in fatal information. Therefore it is important to curb fake news at source and prevent it from spreading to a larger audience. We look at automated techniques for fake news detection from a data mining perspective. We evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset. The classification algorithms are based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). We also evaluate the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabelled covid tweets corpus. We report the best accuracy of 98.41% on the Covid-19 Fake news detection dataset.