A Holistic Framework for Analyzing the COVID-19 Vaccine Debate
This addresses the infodemic of low-quality information affecting public health decisions during the pandemic, but it is incremental as it builds on existing analysis methods.
The paper tackles the problem of analyzing the COVID-19 vaccine debate by proposing a holistic framework that connects stance and reason analysis with moral sentiment analysis, showing it provides reliable predictions in low-supervision settings.
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.