Classifying the Evolving Mask Debate: A Transferable Machine Learning Framework

Abstract

Anti-maskers represent a community of people that opposes the use of face masks on grounds that they infringe personal freedoms. This community has thoroughly exploited the convenience and reach of online social media platforms such as Facebook and Twitter to spread discordant information about the ineffectiveness and harm caused by masks in order to persuade people to shun their use. Automatic detection and demoting of anti-mask tweets is thus necessary to limit their damage. This is challenging because the mask dialogue continuously evolves with creative arguments that embed emerging knowledge about the virus, changing socio-political landscape, and present policies of public health officers and organizations. Therefore, this paper builds a transferrable machine learning framework that can separate between anti-mask and pro-mask tweets from longitudinal data collected at four epochs during the pandemic. The framework extracts content, emotional, and engagement features that faithfully capture the patterns that are relevant to anti-mask rhetoric, but ignores those related to contextual details. It trains two ensemble learners and two neural network architectures using these features. Ensemble classifiers can identify anti-mask tweets with approximately 80% accuracy and F1-score from both individual and combined data sets. The invariant linguistic features extracted by the framework can thus form the basis of automated classifiers that can efficiently separate other types of falsehoods and misinformation from huge volumes of social media data.