  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.
