"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media
This addresses the need for timely and cost-effective monitoring of vaccination attitudes for public health efforts, though it is incremental as it builds on existing methods with network data integration.
The authors tackled the problem of vaccine hesitancy by developing a deep learning framework to extract and track public vaccination attitudes from social media posts in near real time, achieving up to a 23% performance improvement over state-of-the-art text-only models.
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.