NAACL 2022long34 citations
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate
Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser
Abstract
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.
BibTeX
@inproceedings{pacheco-etal-2022-holistic,
title = "A Holistic Framework for Analyzing the {COVID}-19 Vaccine Debate",
author = "Pacheco, Maria Leonor and
Islam, Tunazzina and
Mahajan, Monal and
Shor, Andrey and
Yin, Ming and
Ungar, Lyle and
Goldwasser, Dan",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.427/",
doi = "10.18653/v1/2022.naacl-main.427",
pages = "5821--5839"
}