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

TL;DR

This paper introduces a novel framework for classifying vaccine sentiment tweets by integrating domain-specific language models, commonsense knowledge, and metadata into a context-aware attentive GRU, improving accuracy over existing models.

Contribution

The study presents an end-to-end model combining domain-specific language modeling, commonsense knowledge, and metadata to enhance vaccine sentiment classification on Twitter.

Findings

Outperforms state-of-the-art models in vaccine sentiment classification

Effective use of domain-specific language models and commonsense knowledge

Improved accuracy in identifying pro-, anti-, and neutral vaccine tweets

Abstract

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.