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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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          A 61-million-person experiment in social influence and political mobilization.

          Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between 'close friends' who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.
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            Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures.

            We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses--which is consistent with the effects of sleep and circadian rhythm--and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.
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              A unified theory of urban living.

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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
                : e1500779
                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
                Author information
                http://orcid.org/0000-0001-7252-1524
                http://orcid.org/0000-0003-1127-2231
                http://orcid.org/0000-0001-7085-9994
                http://orcid.org/0000-0001-7795-1638
                Article
                1500779
                10.1126/sciadv.1500779
                4803483
                27034978
                010d2768-cb9a-4257-89be-58c8426a15a7
                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.

                History
                : 12 June 2015
                : 22 January 2016
                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
                Custom metadata
                Meann Ramirez

                social media,social networks,climate change adaptation,disaster management

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