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

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › 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.

Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

Original language	English
Article number	e233
Journal	Proceedings of the Association for Information Science and Technology
Volume	57
Issue number	1
Online published	22 Oct 2020
DOIs	https://doi.org/10.1002/pra2.233
Publication status	Published - 2020
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Keywords

COVID-19
pandemic
temporal differences
topic modeling
twitter

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10.1002/pra2.233

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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. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.",

keywords = "COVID-19, pandemic, temporal differences, topic modeling, twitter",

author = "Han Zheng and Goh, {Dion H.-L.} and Lee, {Chei S.} and Lee, {Edmund W. J.} and Theng, {Yin L.}",

year = "2020",

doi = "10.1002/pra2.233",

language = "English",

volume = "57",

journal = "Proceedings of the Association for Information Science and Technology",

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TY - JOUR

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.

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. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

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. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

KW - COVID-19

KW - pandemic

KW - temporal differences

KW - topic modeling

KW - twitter

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85117058754&origin=recordpage

U2 - 10.1002/pra2.233

DO - 10.1002/pra2.233

M3 - RGC 21 - Publication in refereed journal

C2 - 33173810

SN - 2373-9231

VL - 57

JO - Proceedings of the Association for Information Science and Technology

JF - Proceedings of the Association for Information Science and Technology

IS - 1

M1 - e233

ER -

Uncovering temporal differences in COVID-19 tweets

Abstract

UN SDGs

Research Keywords

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