# Detecting Propaganda on the Sentence Level during the COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2108.12269 · HTML: https://mlsanity.com/paper/2108.12269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 31 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Arming the public with artificial intelligence to counter social bots](https://mlsanity.com/paper/1901.00912/) (2019)
- [Assessing the risks of "infodemics" in response to COVID-19 epidemics](https://mlsanity.com/paper/2004.03997/) (2020)
- [The History of Digital Spam](https://mlsanity.com/paper/1908.06173/) (2019)
- [What Types of COVID-19 Conspiracies are Populated by Twitter Bots?](https://mlsanity.com/paper/2004.09531/) (2020)
- ["Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19](https://mlsanity.com/paper/2004.04046/) (2020)
- [Content and Network Dynamics Behind Egyptian Political Polarization on Twitter](https://mlsanity.com/paper/1410.3097/) (2014)
- [Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic](https://mlsanity.com/paper/2006.04816/) (2020)

## Abstract (arXiv)

The spread of misinformation, conspiracy, and questionable content and information manipulation by foreign adversaries on social media has surged along with the COVID-19 pandemic. Such malicious cyber-enabled actions may cause increasing social polarization, health crises, and property loss. In this paper, using fine-tuned contextualized embedding trained on Reddit, we tackle the detection of the propaganda of such user accounts and their targeted issues on Twitter during March 2020 when the COVID-19 epidemic became recognized as a pandemic. Our result shows that the pro-China group appeared to be tweeting 35 to 115 times more than the neutral group. At the same time, neutral groups were tweeting more positive-attitude content and voicing alarm for the COVID-19 situation. The pro-China group was also using more call-for-action words on political issues not necessarily China-related.