Records |
Author |
Hemant Purohit; Jennifer Chan |
Title |
Classifying User Types on Social Media to inform Who-What-Where Coordination during Crisis Response |
Type |
Conference Article |
Year |
2017 |
Publication |
Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2017 |
Volume |
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Issue |
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Pages |
656-665 |
Keywords |
User Classification, Social Media, Crisis Coordination, Organization, Organization-affiliated |
Abstract |
Timely information is essential for better dynamic situational awareness, which leads to efficient resource planning, coordination, and action. However, given the scale and outreach of social media�a key information sharing platform during crises, diverse types of users participate in discussions during crises, which affect the vetting of information for dynamic situational awareness and response coordination activities. In this paper, we present a user analysis on Twitter during crises for three major user types�Organization, Organizationaffiliated (a person�s self-identifying affiliation with an organization in his/her profile), and Non-affiliated (person not identifying any affiliation), by first classifying users and then presenting their communication patterns during two recent crises. Our analysis shows distinctive patterns of the three user types for participation and communication on social media during crises. Such a user-centric approach to study information sharing during crisis events can act as a precursor to deeper domain-driven content analysis for response agencies. |
Address |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Albi, France |
Editor |
Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
Language |
Englisg |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
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Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
14th International Conference on Information Systems for Crisis Response And Management |
Notes |
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Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2200 |
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Author |
Yasas Senarath; Jennifer Chan; Hemant Purohit; Ozlem Uzuner |
Title |
Evaluating the Relevance of UMLS Knowledge Base for Public Health Informatics during Disasters |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
97-105 |
Keywords |
Public Health, Disaster Informatics, Health Informatics, UMLS, Metathesaurus |
Abstract |
During disasters public health organizations increasingly face challenges in acquiring and transforming real-time data into knowledge about the dynamic public health needs. Resources on the internet can provide valuable information for extracting knowledge that can help improve decisions which will ultimately result in targeted and efficient health services. Digital content such as online articles, blogs, and social media are some of such information sources that could be leveraged to improve the health care systems during disasters. To efficiently and accurately identify relevant disaster health information, extraction tools require a common vocabulary that is aligned to the health domain so that the knowledge from these unstructured digital sources can be accurately structured and organized. In this paper, we study the degree to which the Unified Medical Language System (UMLS) contains relevant disaster, public health, and medical concepts for which public health information in disaster domain could be extracted from digital sources. |
Address |
George Mason University; Northwestern University; George Mason University; George Mason University |
Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-61-5 |
ISBN |
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Medium |
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Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
ywijesu@gmu.edu |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2317 |
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