AMIR: An Automated MisInformation Rebuttal System — A COVID-19 Vaccination Datasets based Exposition

作     者：Sharma, Shakshi Datta, Anwitaman Sharma, Rajesh 

作者机构：School of Artificial Intelligence Bennett University Greater Noida India College of Computing and Data Science Nanyang Technological University Singapore Institute of Computer Science University of Tartu Estonia 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：COVID 19 

摘      要：Misinformation has emerged as a major societal threat in the recent years in general;specifically in the context of the COVID-19 pandemic, it has wrecked havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable solutions for combating misinformation are the need of the hour. This work explored how existing information obtained from social media and augmented with more curated fact checked data repositories can be harnessed to facilitate automated rebuttal of misinformation at scale. While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19. It leverages two publicly available datasets, viz. FaCov (fact-checked articles) [1] and misleading (social media Twitter) [2] data on COVID-19 Vaccination. Copyright © 2023, The Authors. All rights reserved.