The State of Infodemic on Twitter

TL;DR

This paper explores the spread of COVID-19 misinformation on Twitter, analyzing user behavior and employing machine learning and NLP techniques to detect false information in tweets.

Contribution

It provides an exploratory analysis of misinformation spread on Twitter and introduces models for identifying misinformation using NLP methods.

Findings

Identification of misinformation patterns on Twitter

Effective machine learning models for misinformation detection

Insights into user behavior related to COVID-19 misinformation

Abstract

Following the wave of misinterpreted, manipulated and malicious information growing on the Internet, the misinformation surrounding COVID-19 has become a paramount issue. In the context of the current COVID-19 pandemic, social media posts and platforms are at risk of rumors and misinformation in the face of the serious uncertainty surrounding the virus itself. At the same time, the uncertainty and new nature of COVID-19 means that other unconfirmed information that may appear "rumored" may be an important indicator of the behavior and impact of this new virus. Twitter, in particular, has taken a center stage in this storm where Covid-19 has been a much talked about subject. We have presented an exploratory analysis of the tweets and the users who are involved in spreading misinformation and then delved into machine learning models and natural language processing techniques to identify if a tweet contains misinformation.