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

Big data COVID-19 Data mining Pandemic Social media

Journal

International journal of applied earth observation and geoinformation : ITC journal
ISSN: 1569-8432
Titre abrégé: Int J Appl Earth Obs Geoinf
Pays: Netherlands
ID NLM: 101568907

Informations de publication

Date de publication:
Sep 2022
Historique:
received: 26 03 2022
revised: 17 06 2022
accepted: 05 08 2022
entrez: 29 8 2022
pubmed: 30 8 2022
medline: 30 8 2022
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 36035895
doi: 10.1016/j.jag.2022.102967
pii: S1569-8432(22)00160-1
pmc: PMC9391053
doi:

Types de publication

Journal Article

Langues

eng

Pagination

102967

Informations de copyright

© 2022 The Author(s).

Déclaration de conflit d'intérêts

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.

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Auteurs

Xiao Huang (X)

Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA.

Siqin Wang (S)

School of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia.

Mengxi Zhang (M)

Department of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA.

Tao Hu (T)

Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA.

Alexander Hohl (A)

Department of Geography, The University of Utah, Salt Lake City, UT 84112, USA.

Bing She (B)

Institute for social research, University of Michigan, Ann Arbor, MI 48109, USA.

Xi Gong (X)

Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.

Jianxin Li (J)

School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia.

Xiao Liu (X)

School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia.

Oliver Gruebner (O)

Department of Geography, University of Zurich, Zürich CH-8006, Switzerland.

Regina Liu (R)

Department of Biology, Mercer University, Macon, GA 31207, USA.

Xiao Li (X)

Texas A&M Transportation Institute, Bryan, TX 77807, USA.

Zhewei Liu (Z)

Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.

Xinyue Ye (X)

Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA.

Zhenlong Li (Z)

Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA.

Classifications MeSH