@inproceedings{loon-etal-2020-explaining,
title = "Explaining the Trump Gap in Social Distancing Using {COVID} Discourse",
author = "Loon, Austin Van and
Stewart, Sheridan and
Waldon, Brandon and
Lakshmikanth, Shrinidhi K and
Shah, Ishan and
Guntuku, Sharath Chandra and
Sherman, Garrick and
Zou, James and
Eichstaedt, Johannes",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.10",
doi = "10.18653/v1/2020.nlpcovid19-2.10",
abstract = "Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities{'} response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the {``}{''}Trump Gap{''}, or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.",
}
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<abstract>Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “”Trump Gap”, or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.</abstract>
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%0 Conference Proceedings
%T Explaining the Trump Gap in Social Distancing Using COVID Discourse
%A Loon, Austin Van
%A Stewart, Sheridan
%A Waldon, Brandon
%A Lakshmikanth, Shrinidhi K.
%A Shah, Ishan
%A Guntuku, Sharath Chandra
%A Sherman, Garrick
%A Zou, James
%A Eichstaedt, Johannes
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F loon-etal-2020-explaining
%X Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “”Trump Gap”, or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.
%R 10.18653/v1/2020.nlpcovid19-2.10
%U https://aclanthology.org/2020.nlpcovid19-2.10
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.10
Markdown (Informal)
[Explaining the Trump Gap in Social Distancing Using COVID Discourse](https://aclanthology.org/2020.nlpcovid19-2.10) (Loon et al., NLP-COVID19 2020)
ACL
- Austin Van Loon, Sheridan Stewart, Brandon Waldon, Shrinidhi K Lakshmikanth, Ishan Shah, Sharath Chandra Guntuku, Garrick Sherman, James Zou, and Johannes Eichstaedt. 2020. Explaining the Trump Gap in Social Distancing Using COVID Discourse. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.