# Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News

arXiv: https://arxiv.org/abs/2209.03162 · HTML: https://mlsanity.com/paper/2209.03162/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- ["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)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration](https://mlsanity.com/paper/2004.03688/) (2020)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [Weak Supervision for Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/1912.12520/) (2019)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [Multimodal Fake News Detection](https://mlsanity.com/paper/2112.04831/) (2021)
- [The Rise of Social Botnets: Attacks and Countermeasures](https://mlsanity.com/paper/1603.02714/) (2016)

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

- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)

## Abstract (arXiv)

COVID-19 impacted every part of the world, although the misinformation about the outbreak traveled faster than the virus. Misinformation spread through online social networks (OSN) often misled people from following correct medical practices. In particular, OSN bots have been a primary source of disseminating false information and initiating cyber propaganda. Existing work neglects the presence of bots that act as a catalyst in the spread and focuses on fake news detection in 'articles shared in posts' rather than the post (textual) content. Most work on misinformation detection uses manually labeled datasets that are hard to scale for building their predictive models. In this research, we overcome this challenge of data scarcity by proposing an automated approach for labeling data using verified fact-checked statements on a Twitter dataset. In addition, we combine textual features with user-level features (such as followers count and friends count) and tweet-level features (such as number of mentions, hashtags and urls in a tweet) to act as additional indicators to detect misinformation. Moreover, we analyzed the presence of bots in tweets and show that bots change their behavior over time and are most active during the misinformation campaign. We collected 10.22 Million COVID-19 related tweets and used our annotation model to build an extensive and original ground truth dataset for classification purposes. We utilize various machine learning models to accurately detect misinformation and our best classification model achieves precision (82%), recall (96%), and false positive rate (3.58%). Also, our bot analysis indicates that bots generated approximately 10% of misinformation tweets. Our methodology results in substantial exposure of false information, thus improving the trustworthiness of information disseminated through social media platforms.