Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU
This work addresses vaccine hesitancy monitoring for public health by improving sentiment classification on social media, though it is incremental as it builds on existing methods with specific enhancements.
The authors tackled the problem of classifying vaccine sentiment in tweets by developing a novel framework that integrates domain-specific language models and commonsense knowledge into a context-aware attentive GRU, achieving state-of-the-art performance on two Twitter datasets for identifying pro-vaccine, anti-vaccine, and neutral tweets.
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.