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      Performance of Social Network Sensors During Hurricane Sandy

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          Abstract

          Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow, and a mean to derive early-warning sensors, improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioural properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in user's network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays significant role in determining the scale of such advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility of implementing a simple "sentiment sensing" technique to detect and locate disasters.

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          Most cited references28

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          Lexicon-Based Methods for Sentiment Analysis

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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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              Earthquake shakes Twitter users

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                Author and article information

                Journal
                2014-02-11
                2014-06-20
                Article
                10.1371/journal.pone.0117288
                1402.2482
                325d5d33-224b-4dfb-aa6f-78e1f47e57e7

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                cs.SI physics.soc-ph

                Social & Information networks,General physics
                Social & Information networks, General physics

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