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


Huang, Xiao; Wang, Siqin; Zhang, Mengxi; Hu, Tao; Hohl, Alexander; She, Bing; Gong, Xi; Li, Jianxin; Liu, Xiao; Gruebner, Oliver; Liu, Regina; Li, Xiao; Liu, Zhewei; Ye, Xinyue; Li, Zhenlong (2022). Social media mining under the COVID-19 context: Progress, challenges, and opportunities. International Journal of Applied Earth Observation and Geoinformation, 113:102967.

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 repro�ducible 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.

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 repro�ducible 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Global and Planetary Change
Physical Sciences > Earth-Surface Processes
Physical Sciences > Computers in Earth Sciences
Physical Sciences > Management, Monitoring, Policy and Law
Uncontrolled Keywords:Management, Monitoring, Policy and Law, Computers in Earth Sciences, Earth-Surface Processes, Global and Planetary Change
Language:English
Date:1 September 2022
Deposited On:15 Sep 2022 11:37
Last Modified:27 Jun 2024 01:39
Publisher:Elsevier
ISSN:0303-2434
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.jag.2022.102967
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)