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      Rapid assessment of disaster damage using social media activity

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          Abstract

          Researchers show a correlation between per-capita social media activity and disaster damage, facilitating its rapid assessment.

          Abstract

          Could social media data aid in disaster response and damage assessment? Countries face both an increasing frequency and an increasing intensity of natural disasters resulting from climate change. During such events, citizens turn to social media platforms for disaster-related communication and information. Social media improves situational awareness, facilitates dissemination of emergency information, enables early warning systems, and helps coordinate relief efforts. In addition, the spatiotemporal distribution of disaster-related messages helps with the real-time monitoring and assessment of the disaster itself. We present a multiscale analysis of Twitter activity before, during, and after Hurricane Sandy. We examine the online response of 50 metropolitan areas of the United States and find a strong relationship between proximity to Sandy’s path and hurricane-related social media activity. We show that real and perceived threats, together with physical disaster effects, are directly observable through the intensity and composition of Twitter’s message stream. We demonstrate that per-capita Twitter activity strongly correlates with the per-capita economic damage inflicted by the hurricane. We verify our findings for a wide range of disasters and suggest that massive online social networks can be used for rapid assessment of damage caused by a large-scale disaster.

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          Most cited references 26

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          Social science. Computational social science.

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            Changes in tropical cyclone number, duration, and intensity in a warming environment.

            We examined the number of tropical cyclones and cyclone days as well as tropical cyclone intensity over the past 35 years, in an environment of increasing sea surface temperature. A large increase was seen in the number and proportion of hurricanes reaching categories 4 and 5. The largest increase occurred in the North Pacific, Indian, and Southwest Pacific Oceans, and the smallest percentage increase occurred in the North Atlantic Ocean. These increases have taken place while the number of cyclones and cyclone days has decreased in all basins except the North Atlantic during the past decade.
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              Big data. The parable of Google Flu: traps in big data analysis.

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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                March 2016
                11 March 2016
                : 2
                : 3
                Affiliations
                [1 ]National Information and Communications Technology Australia, Melbourne, Victoria 3003, Australia.
                [2 ]Faculty of Information Technology, Monash University, Melbourne, Victoria 3145, Australia.
                [3 ]Data61, Commonwealth Scientific and Industrial Research Organization, Clayton, Victoria 3168, Australia.
                [4 ]Department of Political Science, University of California San Diego, La Jolla, CA 92093, USA.
                [5 ]Center for Marine Biodiversity and Conservation, Scripps Institution of Oceanography, La Jolla, CA 92093, USA.
                [6 ]Department of Mathematics and GISC, Universidad Carlos III de Madrid, Madrid, Leganés 28911, Spain.
                [7 ]Research School of Computer Science, Australian National University, Canberra, Australian Capital Territory 0200, Australia.
                [8 ]Industrial and Operations Engineering, Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109–2117, USA.
                [9 ]Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.
                [10 ]Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.
                Author notes
                [* ]Corresponding author. E-mail: manuel.cebrian@ 123456nicta.com.au
                Article
                1500779
                10.1126/sciadv.1500779
                4803483
                27034978
                Copyright © 2016, The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                Funding
                Funded by: Spanish Ministry of Science and Technology;
                Award ID: ID0E4MDK5475
                Award ID: FIS2013-47532-C3-3-P
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation (US);
                Award ID: ID0ERSDK5476
                Award ID: DGE0707423
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation (US);
                Award ID: ID0E6YDK5477
                Award ID: 1424091
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006754, U.S. Army Research Laboratory (US);
                Award ID: ID0EN6DK5478
                Award ID: W911NF-09-2-0053
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006754, U.S. Army Research Laboratory (US);
                Award ID: ID0ESEEK5479
                Award ID: W911NF-11-1-0363
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation (US);
                Award ID: ID0EXJEK5480
                Award ID: 0905645
                Award Recipient :
                Funded by: DARPA/Lockheed Martin Guard Dog Programme;
                Award ID: ID0E3OEK5481
                Award ID: PO 4100149822
                Award Recipient :
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Disaster Management
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                Meann Ramirez

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