Unmasking the Mask Debate on Social Media

Abstract

Masks are believed to slow the spread of Covid-19, and can prevent many deaths, yet this inexpensive, common sense public health measure has ignited a fierce debate in the U.S.. Opponents of masks or anti-maskers have resorted to measures such as organizing protests and marches to make their views public. They have also taken to social media platforms to vigorously argue against the use of masks, and spread misinformation, lies, and myths regarding their use. Even with the advent of vaccines, masks are still likely to be recommended for a long time. It is therefore necessary to identify those tweets that spread falsehoods regarding the use and effectiveness of masks in order to limit their appeal and damage. This paper proposes a classification framework to detect anti-mask tweets from social media dialogue shared on Twitter during the months of July and August 2020. The framework relies on popular machine learning models

trained using a combination of linguistic, auxiliary, psycho- linguistic and sentiment features for detection. The proposed

classification framework can detect anti-mask tweets with excellent accuracy of over 90%, and hence, it can be used to tag tweets that sow misinformation about masks before they spread through the ether and influence people.