# A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection

arXiv: https://arxiv.org/abs/2101.03545 · HTML: https://mlsanity.com/paper/2101.03545/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 10 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://mlsanity.com/paper/1907.12412/) (2019)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)

## Built on by (most cited first, 10 of 10)

- [Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19](https://mlsanity.com/paper/2208.09578/) (2022)
- [MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning](https://mlsanity.com/paper/2305.12692/) (2023)
- [TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection](https://mlsanity.com/paper/2501.11960/) (2025)
- [A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles](https://mlsanity.com/paper/2104.01791/) (2021)
- [DeepLens: Interactive Out-of-distribution Data Detection in NLP Models](https://mlsanity.com/paper/2303.01577/) (2023)
- [Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations](https://mlsanity.com/paper/2601.17786/) (2026)
- [A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models](https://mlsanity.com/paper/2208.01355/) (2022)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [User Experience Design for Automatic Credibility Assessment of News Content About COVID-19](https://mlsanity.com/paper/2204.13943/) (2022)
- [PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites](https://mlsanity.com/paper/2510.16083/) (2025)

## Abstract (arXiv)

The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world stay connected. With the COVID-19 pandemic raging, social media has become more relevant and widely used than ever before, and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe our Fake News Detection system that automatically identifies whether a tweet related to COVID-19 is "real" or "fake", as a part of CONSTRAINT COVID19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models that has helped us achieve a joint 8th position on the leader board. We have achieved an F1-score of 0.9831 against a top score of 0.9869. Post completion of the competition, we have been able to drastically improve our system by incorporating a novel heuristic algorithm based on username handles and link domains in tweets fetching an F1-score of 0.9883 and achieving state-of-the art results on the given dataset.