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Vaccines are an important public health measure, but vaccine hesitancy andrefusal can create clusters of low vaccine coverage and reduce theeffectiveness of vaccination programs. Social media provides an opportunity toestimate emerging risks to vaccine acceptance by including geographicallocation and detailing vaccine-related concerns. Methods for classifying socialmedia posts, such as vaccine-related tweets, use language models (LMs) trainedon general domain text. However, challenges to measuring vaccine sentiment atscale arise from the absence of tonal stress and gestural cues and may notalways have additional information about the user, e.g., past tweets or socialconnections. Another challenge in LMs is the lack of commonsense knowledge thatare apparent in users metadata, i.e., emoticons, positive and negative wordsetc. In this study, to classify vaccine sentiment tweets with limitedinformation, we present a novel end-to-end framework consisting ofinterconnected components that use domain-specific LM trained onvaccine-related tweets and models commonsense knowledge into a bidirectionalgated recurrent network (CK-BiGRU) with context-aware attention. We furtherleverage syntactical, user metadata and sentiment information to capture thesentiment of a tweet. We experimented using two popular vaccine-related Twitterdatasets and demonstrate that our proposed approach outperformsstate-of-the-art models in identifying pro-vaccine, anti-vaccine and neutraltweets.

Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU