COVIDFakeExplainer - An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News

Aug 4, 2026

Fake news has become a major global challenge intensified by the COVID-19 pandemic, creating an urgent need for preventive and effective tools. Leveraging machine learning, the work positions BERT as the strongest model for COVID-19 fake-news detection. An explainable capability is integrated into the BERT pipeline and exposed via an AWS-hosted cloud API. The system is connected to a browser extension that delivers real-time classifications with interpretable explanations using multiple dataset-driven configurations.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

I. Introduction

Background and impact of fake news during COVID-19

Role of machine learning and need for explainability

Proposed Chrome extension-based explainability system

COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News Dylan Warman School of Computing, Mathematics and Engineering Charles Sturt University NSW, Australia [dwarman@csu.edu.au](dwarman@csu.edu.au) Muhammad Ashad Kabir School of Computing, Mathematics and Engineering Charles Sturt University NSW, Australia [akabir@csu.edu.au](akabir@csu.edu.au) arXiv :2310 . 13890v 1 [ cs . SI] 21 Oct 2023 Abstract—Fake news has emerged as a critical global issue, magnified by the COVID-19 pandemic, underscoring the need for effective preventive tools. Leveraging machine learning, including deep learning techniques, offers promise in combatting fake news. This paper goes beyond by establishing BERT as the superior model for fake news detection and demonstrates its utility as a tool to empower the general populace. We have implemented a browser extension, enhanced with explainability features, enabling real-time identification of fake news and delivering easily interpretable explanations. To achieve this, we have employed two publicly available datasets and created seven distinct data configurations to evaluate three prominent machine learning architectures. Our comprehensive experiments affirm BERT’s exceptional accuracy in detecting COVID-19-related fake news. Furthermore, we have integrated an explainability component into the BERT model and deployed it as a service through Amazon’s cloud API hosting (AWS). We have developed a browser extension that interfaces with the API, allowing users to select and transmit data from web pages, receiving an intelligible classification in return. This paper presents a practical end-to-end solution, highlighting the feasibility of constructing a holistic system for fake news detection, which can significantly benefit society. Index Terms—COVID-19, machine learning, deep learning, fake news, explainability, web application, chrome extension I. INTRODUCTION Fake news is known by many interchangeable names, with the main two being the terms “Fake news” itself and “Misinformation” [1], [2] . These terms are used to mean false or misleading information shared to deceive an individual, group, or population into believing something that is clearly not true, often with political motivations with the intention of damaging public trust. In the context of this article, we refer to all of this as fake news [3] . Social networks provide a platform that millions of people around the world use to communicate and share information ©2023 IEEE. This manuscript has been accepted to publish in the 10th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (IEEE CSDE 2023) . Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. on a daily basis. However, especially at a time of global crisis such as during the COVID-19 pandemic, the amount of Fake news, being shared is staggering. Global statistics indicate that 74% people are very concerned about the amount of fake news they have seen during the pandemic [4], and furthermore, studies have shown that more than 50% of all social media users have spread fake news knowingly or unknowingly [5] . Further research shows that fake news is not more likely tobe shared by robots or artificial intelligence, people were found to be more likely to spread fake news [6] . A potential reason for sharing fake news is identified as our cognitive biases, more specifically our memory biases, and a phenomenon known asthe “false memory effect” [7] . Additionally, there has been shown to be a large disconnect between what people believe and what types of fake news they will share, furthering th

FAQs

为什么该文强调需要检测 COVID-19 假新闻的工具？

COVID-19 假新闻在全球危机期间传播量巨大，并可能像“信息疫情”一样对公共健康与安全造成与病毒相当的风险，因此需要能够提升理解与防护的检测工具。

作者在假新闻检测中采用了哪些关键技术？

文中使用机器学习并重点证明 BERT 在检测 COVID-19 相关假新闻方面具有卓越准确性，同时引入可解释性以帮助理解模型预测依据。

系统如何向用户提供可解释的检测结果？

作者将带可解释性的 BERT 集成到 AWS 托管的云 API 中，浏览器扩展与 API 交互；用户选择并发送网页数据后，会获得可理解的分类与解释。

Aug 4, 2026

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