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Social Media Crowdsourcing for Rapid Damage Assessment Following Sudden-Onset Earthquakes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Social Media Crowdsourcing for Rapid Damage Assessment Following Sudden-Onset Earthquakes./
作者:	Li, Lingyao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	231 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28777485
ISBN:	9798790630675

Social Media Crowdsourcing for Rapid Damage Assessment Following Sudden-Onset Earthquakes.
Li, Lingyao.

Social Media Crowdsourcing for Rapid Damage Assessment Following Sudden-Onset Earthquakes. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 231 p.

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2021.

This item must not be sold to any third party vendors.

Rapid appraisal of damages related to hazard events is important to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections, and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, principally Twitter postings, is investigated to make approximate but rapid early assessments of damages following earthquake disasters. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a text-based damage assessment scale for earthquake damages and then develops a text classification model for rapid damage assessment. The 2019 Ridgecrest, California earthquake sequence is mainly investigated as the case study. Results reveal that Twitter users rapidly responded to this sudden-onset event, and the damage estimation shows temporal and spatial characteristics. The generalization ability of the model is validated through the investigation of damage assessment for another five earthquake events. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks.

ISBN: 9798790630675Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Crowdsourcing

Social Media Crowdsourcing for Rapid Damage Assessment Following Sudden-Onset Earthquakes.
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100 1 $a Li, Lingyao. $3 3685625
245 1 0 $a Social Media Crowdsourcing for Rapid Damage Assessment Following Sudden-Onset Earthquakes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 231 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
500 $a Advisor: Baecher, Gregory;Bensi, Michelle.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Rapid appraisal of damages related to hazard events is important to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections, and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, principally Twitter postings, is investigated to make approximate but rapid early assessments of damages following earthquake disasters. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a text-based damage assessment scale for earthquake damages and then develops a text classification model for rapid damage assessment. The 2019 Ridgecrest, California earthquake sequence is mainly investigated as the case study. Results reveal that Twitter users rapidly responded to this sudden-onset event, and the damage estimation shows temporal and spatial characteristics. The generalization ability of the model is validated through the investigation of damage assessment for another five earthquake events. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks.
590 $a School code: 0117.
650 4 $a Engineering. $3 586835
650 4 $a Geophysics. $3 535228
650 4 $a Geographic information science. $3 3432445
650 4 $a Web studies. $3 2122754
650 4 $a Geological engineering. $3 2122713
653 $a Crowdsourcing
653 $a Damage assessment
653 $a Social media
653 $a Sudden-onset earthquakes
653 $a Text classification
690 $a 0537
690 $a 0646
690 $a 0370
690 $a 0373
690 $a 0466
690 $a 0467
710 2 $a University of Maryland, College Park. $b Civil Engineering. $3 1025664
773 0 $t Dissertations Abstracts International $g 83-08B.
790 $a 0117
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28777485