Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Xiao Huang¹, Siqin Wang², Mengxi Zhang³, Tao Hu⁴, Alexander Hohl⁵, Bing She⁶, Xi Gong⁷, Jianxin Li⁸, Xiao Liu⁸, Oliver Gruebner⁹, Regina Liu¹⁰, Xiao Li¹¹, Zhewei Liu¹², Xinyue Ye¹³, Zhenlong Li¹⁴

Affiliations

¹ Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA.
² School of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia.
³ Department of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA.
⁴ Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA.
⁵ Department of Geography, The University of Utah, Salt Lake City, UT 84112, USA.
⁶ Institute for social research, University of Michigan, Ann Arbor, MI 48109, USA.
⁷ Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
⁸ School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia.
⁹ Department of Geography, University of Zurich, Zürich CH-8006, Switzerland.
¹⁰ Department of Biology, Mercer University, Macon, GA 31207, USA.
¹¹ Texas A&M Transportation Institute, Bryan, TX 77807, USA.
¹² Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
¹³ Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA.
¹⁴ Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA.

PMID: 36035895
PMCID: PMC9391053
DOI: 10.1016/j.jag.2022.102967

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Xiao Huang et al. Int J Appl Earth Obs Geoinf. 2022 Sep.

. 2022 Sep:113:102967.

doi: 10.1016/j.jag.2022.102967. Epub 2022 Aug 19.

Authors

Affiliations

¹ Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA.
² School of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia.
³ Department of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA.
⁴ Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA.
⁵ Department of Geography, The University of Utah, Salt Lake City, UT 84112, USA.
⁶ Institute for social research, University of Michigan, Ann Arbor, MI 48109, USA.
⁷ Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
⁸ School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia.
⁹ Department of Geography, University of Zurich, Zürich CH-8006, Switzerland.
¹⁰ Department of Biology, Mercer University, Macon, GA 31207, USA.
¹¹ Texas A&M Transportation Institute, Bryan, TX 77807, USA.
¹² Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
¹³ Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA.
¹⁴ Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA.

PMID: 36035895
PMCID: PMC9391053
DOI: 10.1016/j.jag.2022.102967

Abstract

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.

Keywords: Big data; COVID-19; Data mining; Pandemic; Social media.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

**Fig. 1**
The structure of this review.

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Social media mining under the COVID-19 context: Progress, challenges, and opportunities

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Social media mining under the COVID-19 context: Progress, challenges, and opportunities

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Conflict of interest statement

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