Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision

TL;DR

This paper introduces a minimally supervised multi-task learning framework to analyze COVID-19 vaccine messaging on Facebook, aiming to identify themes and moral foundations to inform better policymaking during the pandemic.

Contribution

It presents a novel minimal supervision approach for multi-task learning to analyze vaccine-related social media content, focusing on themes and moral foundations.

Findings

Effective identification of vaccine ad themes and moral foundations.

Enhanced understanding of messaging tactics in social media campaigns.

Potential to inform policymakers for better pandemic response strategies.

Abstract

In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.