# g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection

arXiv: https://arxiv.org/abs/2012.11967 · HTML: https://mlsanity.com/paper/2012.11967/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs](https://mlsanity.com/paper/2408.12060/) (2024)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles](https://mlsanity.com/paper/2110.10457/) (2021)
- [A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles](https://mlsanity.com/paper/2104.01791/) (2021)
- [BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets](https://mlsanity.com/paper/2211.09733/) (2022)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)
- [Multilingual Models for Check-Worthy Social Media Posts Detection](https://mlsanity.com/paper/2408.06737/) (2024)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [Detecting Generated Scientific Papers using an Ensemble of Transformer Models](https://mlsanity.com/paper/2209.08283/) (2022)
- [A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation](https://mlsanity.com/paper/2609.00403/) (2026)
- [Automated Evidence Collection for Fake News Detection](https://mlsanity.com/paper/2112.06507/) (2021)
- [tmn at SemEval-2023 Task 9: Multilingual Tweet Intimacy Detection using XLM-T, Google Translate, and Ensemble Learning](https://mlsanity.com/paper/2304.04054/) (2023)
- [tmn at #SMM4H 2023: Comparing Text Preprocessing Techniques for Detecting Tweets Self-reporting a COVID-19 Diagnosis](https://mlsanity.com/paper/2311.00732/) (2023)
- [A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data](https://mlsanity.com/paper/2311.08001/) (2023)

## Abstract (arXiv)

The COVID-19 pandemic has had a huge impact on various areas of human life. Hence, the coronavirus pandemic and its consequences are being actively discussed on social media. However, not all social media posts are truthful. Many of them spread fake news that cause panic among readers, misinform people and thus exacerbate the effect of the pandemic. In this paper, we present our results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News Detection in English. In particular, we propose our approach using the transformer-based ensemble of COVID-Twitter-BERT (CT-BERT) models. We describe the models used, the ways of text preprocessing and adding extra data. As a result, our best model achieved the weighted F1-score of 98.69 on the test set (the first place in the leaderboard) of this shared task that attracted 166 submitted teams in total.