"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media

TL;DR

This paper presents a deep learning framework that analyzes social media posts to track and predict public attitudes towards COVID-19 vaccination in real time, addressing limitations of traditional survey methods.

Contribution

It introduces a novel social media-based approach incorporating social network context to improve attitude detection accuracy and track attitude changes over time.

Findings

Models improved attitude extraction performance by up to 23%.

Framework effectively tracks vaccination attitude evolution in real time.

Potential to forecast individual vaccine hesitancy changes.

Abstract

To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional attitude collection based on surveys is time-consuming and expensive, and cannot follow the fast evolution of vaccination attitudes. We leverage the textual posts on social media to extract and track users' vaccination stances in near real time by proposing a deep learning framework. To address the impact of linguistic features such as sarcasm and irony commonly used in vaccine-related discourses, we integrate into the framework the recent posts of a user's social network neighbours to help detect the user's genuine attitude. Based on our annotated dataset from Twitter, the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to state-of-the-art text-only models. Using this framework, we successfully validate the feasibility of using social media to track the evolution of vaccination attitudes in real life. We further show one practical use of our framework by validating the possibility to forecast a user's vaccine hesitancy changes with information perceived from social media.