  Vaccines are an important public health measure, but vaccine hesitancy and
refusal can create clusters of low vaccine coverage and reduce the
effectiveness of vaccination programs. Social media provides an opportunity to
estimate emerging risks to vaccine acceptance by including geographical
location and detailing vaccine-related concerns. Methods for classifying social
media posts, such as vaccine-related tweets, use language models (LMs) trained
on general domain text. However, challenges to measuring vaccine sentiment at
scale arise from the absence of tonal stress and gestural cues and may not
always have additional information about the user, e.g., past tweets or social
connections. Another challenge in LMs is the lack of commonsense knowledge that
are apparent in users metadata, i.e., emoticons, positive and negative words
etc. In this study, to classify vaccine sentiment tweets with limited
information, we present a novel end-to-end framework consisting of
interconnected components that use domain-specific LM trained on
vaccine-related tweets and models commonsense knowledge into a bidirectional
gated recurrent network (CK-BiGRU) with context-aware attention. We further
leverage syntactical, user metadata and sentiment information to capture the
sentiment of a tweet. We experimented using two popular vaccine-related Twitter
datasets and demonstrate that our proposed approach outperforms
state-of-the-art models in identifying pro-vaccine, anti-vaccine and neutral
tweets.
