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.
CITATION STYLE
Pacheco, M. L., Islam, T., Mahajan, M., Shor, A., Yin, M., Ungar, L., & Goldwasser, D. (2022). A Holistic Framework for Analyzing the COVID-19 Vaccine Debate. In NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 5821–5839). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.naacl-main.427
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