Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media

Oliver Gruebner, Sarah R. Lowe, Martin Sykora, Ketan Shankardass, Subramanian Sv and Sandro Galea
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Oliver Gruebner: Department of Geography, Humboldt-Universität zu Berlin, Berlin 10099, Germany
Sarah R. Lowe: Department of Psychology, Montclair State University, Montclair, NJ 07043, USA
Martin Sykora: Centre for Information Management (CIM), School of Business and Economics (SBE), Loughborough University, Loughborough LE11 3TU, UK
Ketan Shankardass: Department of Health Sciences, Wilfrid Laurier University, Waterloo, ON M5B 1W8, Canada
Subramanian Sv: Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Sandro Galea: School of Public Health, Boston University, Boston, MA 02118, USA

IJERPH, 2018, vol. 15, issue 10, 1-12

Abstract: Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called ”Extracting the Meaning Of Terse Information in a Visualization of Emotion” (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran’s I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care.

Keywords: advanced sentiment analysis; digital epidemiology; geographic information system; geo-social media; hotspots; post-disaster mental health; psychogeography; spatial epidemiology; spatial regimes regression; Twitter data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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