Evidence map›Paper›PMID 36035895›Full record

ArticleInternational journal of applied earth observation and geoinformation : ITC journal2022

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

Xiao Huang, Siqin Wang, Mengxi Zhang, Tao Hu, Alexander Hohl, Bing She, Xi Gong, Jianxin Li, Xiao Liu, Oliver Gruebner and 5 more

Abstract read
In one paragraph

Article in International journal of applied earth observation and geoinformation : ITC journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Xiao HuangDepartment of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA.
Siqin WangSchool of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia.
Mengxi ZhangDepartment of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA.
Tao HuDepartment of Geography, Oklahoma State University, Stillwater, OK 74078, USA.
Alexander HohlDepartment of Geography, The University of Utah, Salt Lake City, UT 84112, USA.
Bing SheInstitute for social research, University of Michigan, Ann Arbor, MI 48109, USA.
Xi GongDepartment of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
Jianxin LiSchool of Information Technology, Deakin University, Geelong, Victoria 3220, Australia.
Xiao LiuSchool of Information Technology, Deakin University, Geelong, Victoria 3220, Australia.
Oliver GruebnerDepartment of Geography, University of Zurich, Zürich CH-8006, Switzerland.
Regina LiuDepartment of Biology, Mercer University, Macon, GA 31207, USA.
Xiao LiTexas A&M Transportation Institute, Bryan, TX 77807, USA.
Zhewei LiuDepartment of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
Xinyue YeDepartment of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA.
Zhenlong LiGeoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Big dataCOVID-19Data miningPandemicSocial media

Identifiers

PMID36035895
PMCPMC9391053

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.