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ISPRS Int. J. Geo-Inf. 2019, 8(3), 111; https://doi.org/10.3390/ijgi8030111

A Twitter Data Credibility Framework—Hurricane Harvey as a Use Case

1
NSF Spatiotemporal Innovation Center and Department of Geography and GeoInformation Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
2
Ankura Consulting Group, LLC, 1220 19th St NW #700, Washington, DC 20036, USA
3
Nanjing University of Information Engineering, Pukou, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Received: 10 January 2019 / Revised: 21 February 2019 / Accepted: 21 February 2019 / Published: 28 February 2019
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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Abstract

Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situation awareness. The framework is derived from crowdsourcing, which states that errors propagated in volunteered information decrease as the number of contributors increases. In the proposed framework, credibility is hierarchically assessed on two tweet levels. The framework was tested using Hurricane Harvey Twitter data, in which situation awareness related tweets were extracted using a set of predefined keywords including power, shelter, damage, casualty, and flood. For each tweet, text messages and associated URLs were integrated to enhance the information completeness. Events were identified by aggregating tweets based on their topics and spatiotemporal characteristics. Credibility for events was calculated and analyzed against the spatial, temporal, and social impacting scales. This framework has the potential to calculate the evolving credibility in real time, providing users insight on the most important and trustworthy events. View Full-Text
Keywords: social media; twitter; credibility; crowdsourcing; hurricane; location extraction; gazetteer; spatiotemporal patterns; natural hazard social media; twitter; credibility; crowdsourcing; hurricane; location extraction; gazetteer; spatiotemporal patterns; natural hazard
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Yang, J.; Yu, M.; Qin, H.; Lu, M.; Yang, C. A Twitter Data Credibility Framework—Hurricane Harvey as a Use Case. ISPRS Int. J. Geo-Inf. 2019, 8, 111.

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