# Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models

arXiv: https://arxiv.org/abs/2103.00747 · HTML: https://mlsanity.com/paper/2103.00747/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [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)
- [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)
- [TI-CNN: Convolutional Neural Networks for Fake News Detection](https://mlsanity.com/paper/1806.00749/) (2018)
- [Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings](https://mlsanity.com/paper/1804.09088/) (2018)
- [Two-path Deep Semi-supervised Learning for Timely Fake News Detection](https://mlsanity.com/paper/2002.00763/) (2020)
- [Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability](https://mlsanity.com/paper/2012.13603/) (2020)
- [Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression](https://mlsanity.com/paper/2006.16942/) (2020)

## Built on by (most cited first, 17 of 17)

- [AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box](https://mlsanity.com/paper/2112.12635/) (2021)
- [Is Trust Correlated With Explainability in AI? A Meta-Analysis](https://mlsanity.com/paper/2504.12529/) (2025)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Predicting Driver Takeover Time in Conditionally Automated Driving](https://mlsanity.com/paper/2107.09545/) (2021)
- [Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study](https://mlsanity.com/paper/2211.15407/) (2022)
- [Fake News Detection After LLM Laundering: Measurement and Explanation](https://mlsanity.com/paper/2501.18649/) (2025)
- [Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Study](https://mlsanity.com/paper/2202.00471/) (2022)
- [A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models](https://mlsanity.com/paper/2208.01355/) (2022)
- [CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models](https://mlsanity.com/paper/2307.12382/) (2023)
- [COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News](https://mlsanity.com/paper/2310.13890/) (2023)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [Wrapper Boxes: Faithful Attribution of Model Predictions to Training Data](https://mlsanity.com/paper/2311.08644/) (2023)
- [VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language Inference](https://mlsanity.com/paper/2410.09455/) (2024)
- [A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation](https://mlsanity.com/paper/2609.00403/) (2026)
- [Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data](https://mlsanity.com/paper/2208.00249/) (2022)
- [MAPX: An explainable model-agnostic framework for the detection of false information on social media networks](https://mlsanity.com/paper/2409.08522/) (2024)

## Abstract (arXiv)

Misinformation of COVID-19 is prevalent on social media as the pandemic unfolds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encoder Representations from Transformers), have achieved great successes in detecting misinformation. In this paper, we proposed an explainable natural language processing model based on DistilBERT and SHAP (Shapley Additive exPlanations) to combat misinformation about COVID-19 due to their efficiency and effectiveness. First, we collected a dataset of 984 claims about COVID-19 with fact checking. By augmenting the data using back-translation, we doubled the sample size of the dataset and the DistilBERT model was able to obtain good performance (accuracy: 0.972; areas under the curve: 0.993) in detecting misinformation about COVID-19. Our model was also tested on a larger dataset for AAAI2021 - COVID-19 Fake News Detection Shared Task and obtained good performance (accuracy: 0.938; areas under the curve: 0.985). The performance on both datasets was better than traditional machine learning models. Second, in order to boost public trust in model prediction, we employed SHAP to improve model explainability, which was further evaluated using a between-subjects experiment with three conditions, i.e., text (T), text+SHAP explanation (TSE), and text+SHAP explanation+source and evidence (TSESE). The participants were significantly more likely to trust and share information related to COVID-19 in the TSE and TSESE conditions than in the T condition. Our results provided good implications in detecting misinformation about COVID-19 and improving public trust.