# Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter

arXiv: https://arxiv.org/abs/2101.05626 · HTML: https://mlsanity.com/paper/2101.05626/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 9 Jan 2021.

## Builds on (most cited first, 20 of 20)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients](https://mlsanity.com/paper/2010.07468/) (2020)
- [Stochastic AUC Maximization with Deep Neural Networks](https://mlsanity.com/paper/1908.10831/) (2019)
- [Scalable Learning of Non-Decomposable Objectives](https://mlsanity.com/paper/1608.04802/) (2016)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Automatically Identifying Fake News in Popular Twitter Threads](https://mlsanity.com/paper/1705.01613/) (2017)
- [A first look at COVID-19 information and misinformation sharing on Twitter](https://mlsanity.com/paper/2003.13907/) (2020)
- [Using Word Embeddings in Twitter Election Classification](https://mlsanity.com/paper/1606.07006/) (2016)
- [Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter](https://mlsanity.com/paper/1707.03778/) (2017)
- [Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions](https://mlsanity.com/paper/2005.08820/) (2020)
- [Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics](https://mlsanity.com/paper/2005.13691/) (2020)
- [ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic](https://mlsanity.com/paper/2012.01462/) (2020)
- [Analysis of misinformation during the COVID-19 outbreak in China: cultural, social and political entanglements](https://mlsanity.com/paper/2005.10414/) (2020)
- [Is this pofma? Analysing public opinion and misinformation in a COVID-19 Telegram group chat](https://mlsanity.com/paper/2010.10113/) (2020)
- [Cultural Convergence: Insights into the behavior of misinformation networks on Twitter](https://mlsanity.com/paper/2007.03443/) (2020)

## Built on by (most cited first, 1 of 1)

- [Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study](https://mlsanity.com/paper/2301.05863/) (2023)

## Abstract (arXiv)

The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset related to COVID-19 misinformation and gold-annotate the tweets into two categories: misinformation or not. Then, we apply eight different traditional and deep machine learning models, with different features including word embeddings and word frequency. The word embedding models (textscFastText and word2vec) exploit more than two million Arabic tweets related to COVID-19. Experiments show that optimizing the area under the curve (AUC) improves the models' performance and the Extreme Gradient Boosting (XGBoost) presents the highest accuracy in detecting COVID-19 misinformation online.