# Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19

arXiv: https://arxiv.org/abs/2208.09578 · HTML: https://mlsanity.com/paper/2208.09578/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 62 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 28 of 28)

- [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)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Multi-Adversarial Domain Adaptation](https://mlsanity.com/paper/1809.02176/) (2018)
- [Regularized Learning for Domain Adaptation under Label Shifts](https://mlsanity.com/paper/1903.09734/) (2019)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (2019)
- [DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning](https://mlsanity.com/paper/1809.06416/) (2018)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [Learning Hierarchical Discourse-level Structure for Fake News Detection](https://mlsanity.com/paper/1903.07389/) (2019)
- [Contrastive Domain Adaptation for Question Answering using Limited Text Corpora](https://mlsanity.com/paper/2108.13854/) (2021)
- [Automatically Identifying Fake News in Popular Twitter Threads](https://mlsanity.com/paper/1705.01613/) (2017)
- [Adversarial Domain Adaptation for Stance Detection](https://mlsanity.com/paper/1902.02401/) (2019)
- [A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03545/) (2021)
- [The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?](https://mlsanity.com/paper/2008.05701/) (2020)

## Built on by (most cited first, 15 of 15)

- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer](https://mlsanity.com/paper/2308.09499/) (2023)
- [MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning](https://mlsanity.com/paper/2305.12692/) (2023)
- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [Domain Adaptation for Question Answering via Question Classification](https://mlsanity.com/paper/2209.04998/) (2022)
- [Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2210.05498/) (2022)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection](https://mlsanity.com/paper/2404.01336/) (2024)
- [Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup](https://mlsanity.com/paper/2210.03250/) (2022)
- [Fake News Detection Through Graph-based Neural Networks: A Survey](https://mlsanity.com/paper/2307.12639/) (2023)
- [QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation](https://mlsanity.com/paper/2210.10861/) (2022)
- [Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning](https://mlsanity.com/paper/2305.17373/) (2023)
- [A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection](https://mlsanity.com/paper/2304.01492/) (2023)
- [Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detection](https://mlsanity.com/paper/2506.04739/) (2025)
- [Cross-Domain Fake News Detection on Unseen Domains via LLM-Based Domain-Aware User Modeling](https://mlsanity.com/paper/2602.01726/) (2026)

## Abstract (arXiv)

Despite recent progress in improving the performance of misinformation detection systems, classifying misinformation in an unseen domain remains an elusive challenge. To address this issue, a common approach is to introduce a domain critic and encourage domain-invariant input features. However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e.g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation. In this paper, we propose contrastive adaptation network for early misinformation detection (CANMD). Specifically, we leverage pseudo labeling to generate high-confidence target examples for joint training with source data. We additionally design a label correction component to estimate and correct the label shifts (i.e., class priors) between the source and target domains. Moreover, a contrastive adaptation loss is integrated in the objective function to reduce the intra-class discrepancy and enlarge the inter-class discrepancy. As such, the adapted model learns corrected class priors and an invariant conditional distribution across both domains for improved estimation of the target data distribution. To demonstrate the effectiveness of the proposed CANMD, we study the case of COVID-19 early misinformation detection and perform extensive experiments using multiple real-world datasets. The results suggest that CANMD can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.