@inproceedings{zamani-etal-2020-understanding,
title = "Understanding Weekly {COVID}-19 Concerns through Dynamic Content-Specific {LDA} Topic Modeling",
author = "Zamani, Mohammadzaman and
Schwartz, H. Andrew and
Eichstaedt, Johannes and
Guntuku, Sharath Chandra and
Virinchipuram Ganesan, Adithya and
Clouston, Sean and
Giorgi, Salvatore",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Volkova, Svitlana",
booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcss-1.21",
doi = "10.18653/v1/2020.nlpcss-1.21",
pages = "193--198",
abstract = "The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.",
}
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%0 Conference Proceedings
%T Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling
%A Zamani, Mohammadzaman
%A Schwartz, H. Andrew
%A Eichstaedt, Johannes
%A Guntuku, Sharath Chandra
%A Virinchipuram Ganesan, Adithya
%A Clouston, Sean
%A Giorgi, Salvatore
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zamani-etal-2020-understanding
%X The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.
%R 10.18653/v1/2020.nlpcss-1.21
%U https://aclanthology.org/2020.nlpcss-1.21
%U https://doi.org/10.18653/v1/2020.nlpcss-1.21
%P 193-198
Markdown (Informal)
[Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling](https://aclanthology.org/2020.nlpcss-1.21) (Zamani et al., NLP+CSS 2020)
ACL