# Detecting Conspiracy Theory Against COVID-19 Vaccines

arXiv: https://arxiv.org/abs/2211.13003 · HTML: https://mlsanity.com/paper/2211.13003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## 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)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Deceiving Google's Perspective API Built for Detecting Toxic Comments](https://mlsanity.com/paper/1702.08138/) (2017)
- [Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media](https://mlsanity.com/paper/1804.04257/) (2018)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [Peer to Peer Hate: Hate Speech Instigators and Their Targets](https://mlsanity.com/paper/1804.04649/) (2018)
- [A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts](https://mlsanity.com/paper/2001.05326/) (2020)
- [Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence](https://mlsanity.com/paper/2108.13293/) (2021)
- [Improving BERT Performance for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2010.11731/) (2020)
- [On the Globalization of the QAnon Conspiracy Theory Through Telegram](https://mlsanity.com/paper/2105.13020/) (2021)
- [Science vs Conspiracy: collective narratives in the age of (mis)information](https://mlsanity.com/paper/1408.1667/) (2014)

## Built on by (most cited first, 2 of 2)

- [UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers](https://mlsanity.com/paper/2309.16275/) (2023)
- [Multilingual Financial Fraud Detection Using Machine Learning and Transformer Models: A Bangla-English Study](https://mlsanity.com/paper/2603.11358/) (2026)

## Abstract (arXiv)

Since the beginning of the vaccination trial, social media has been flooded with anti-vaccination comments and conspiracy beliefs. As the day passes, the number of COVID- 19 cases increases, and online platforms and a few news portals entertain sharing different conspiracy theories. The most popular conspiracy belief was the link between the 5G network spreading COVID-19 and the Chinese government spreading the virus as a bioweapon, which initially created racial hatred. Although some disbelief has less impact on society, others create massive destruction. For example, the 5G conspiracy led to the burn of the 5G Tower, and belief in the Chinese bioweapon story promoted an attack on the Asian-Americans. Another popular conspiracy belief was that Bill Gates spread this Coronavirus disease (COVID-19) by launching a mass vaccination program to track everyone. This Conspiracy belief creates distrust issues among laypeople and creates vaccine hesitancy. This study aims to discover the conspiracy theory against the vaccine on social platforms. We performed a sentiment analysis on the 598 unique sample comments related to COVID-19 vaccines. We used two different models, BERT and Perspective API, to find out the sentiment and toxicity of the sentence toward the COVID-19 vaccine.