Uncovering temporal differences in COVID-19 tweets

Han Zheng; Dion H.L. Goh; Chei S. Lee; Edmund W.J. Lee; Yin L. Theng

doi:10.1002/pra2.233

Uncovering temporal differences in COVID-19 tweets

Han Zheng^*, Dion H.L. Goh, Chei S. Lee, Edmund W.J. Lee, Yin L. Theng

^*Corresponding author for this work

Nanyang Technological University

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people's discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light.

Original language	English
Article number	e233
Journal	Proceedings of the Association for Information Science and Technology
Volume	57
Issue number	1
DOIs	https://doi.org/10.1002/pra2.233
Publication status	Published - 2020
Externally published	Yes

Bibliographical note

Publisher Copyright:
83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

ASJC Scopus Subject Areas

General Computer Science
Library and Information Sciences

Keywords

COVID-19
pandemic
temporal differences
topic modeling
twitter

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10.1002/pra2.233

Cite this

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title = "Uncovering temporal differences in COVID-19 tweets",

abstract = "In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people's discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light.",

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note = "Publisher Copyright: 83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.",

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T1 - Uncovering temporal differences in COVID-19 tweets

AU - Zheng, Han

AU - Goh, Dion H.L.

AU - Lee, Chei S.

AU - Lee, Edmund W.J.

AU - Theng, Yin L.

N1 - Publisher Copyright: 83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

PY - 2020

Y1 - 2020

N2 - In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people's discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light.

AB - In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people's discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light.

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Uncovering temporal differences in COVID-19 tweets

Abstract

Bibliographical note

ASJC Scopus Subject Areas

Keywords

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