AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System

TL;DR

This paper presents AMIR, a scalable system that uses curated fact-checked data and social media information to automate rebuttal of COVID-19 vaccine misinformation on Twitter.

Contribution

It introduces a novel approach combining social media and fact-checked datasets for automated misinformation rebuttal specific to COVID-19 vaccines.

Findings

Demonstrates effectiveness in rebutting COVID-19 vaccine misinformation

Leverages publicly available datasets for scalable solutions

Provides a proof of concept for automated misinformation mitigation

Abstract

Misinformation has emerged as a major societal threat in 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) and misleading (social media Twitter) data on COVID-19 Vaccination.