# Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF

arXiv: https://arxiv.org/abs/2205.00377 · HTML: https://mlsanity.com/paper/2205.00377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task](https://mlsanity.com/paper/1910.14353/) (2019)

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

- [Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions](https://mlsanity.com/paper/2502.00339/) (2025)
- [A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit](https://mlsanity.com/paper/2502.09097/) (2025)

## Abstract (arXiv)

The sharing of fake news and conspiracy theories on social media has wide-spread negative effects. By designing and applying different machine learning models, researchers have made progress in detecting fake news from text. However, existing research places a heavy emphasis on general, common-sense fake news, while in reality fake news often involves rapidly changing topics and domain-specific vocabulary. In this paper, we present our methods and results for three fake news detection tasks at MediaEval benchmark 2021 that specifically involve COVID-19 related topics. We experiment with a group of text-based models including Support Vector Machines, Random Forest, BERT, and RoBERTa. We find that a pre-trained transformer yields the best validation results, but a randomly initialized transformer with smart design can also be trained to reach accuracies close to that of the pre-trained transformer.