Garske et al - ijerph - 2021 - Final Accepted and Published.pdf (1.74 MB)

Space-time dependence of emotions on Twitter after a natural disaster

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journal contribution
posted on 17.05.2021, 13:43 by Sonja I Garske, Suzanne Elayan, Martin Sykora, Tamar Edry, Linus B Grabenhenrich, Sandro Galea, Sarah R Lowe, Oliver Gruebner
Natural disasters can have significant consequences for population mental health. Using a digital spatial epidemiologic approach, this study documents emotional changes over space and time in the context of a large-scale disaster. Our aims were to a) explore the spatial distribution of negative emotional expressions of Twitter users before, during, and after Superstorm Sandy in New York City (NYC) in 2012 and b) examine potential correlations between socioeconomic status and infrastructural damage with negative emotional expressions across NYC census tracts over time. A total of 984,311 geo-referenced tweets with negative basic emotions (anger, disgust, fear, sadness, shame) were collected and assigned to the census tracts within NYC boroughs between 8 October and 18 November 2012. Global and local univariate and bivariate Moran’s I statistics were used to analyze the data. We found local spatial clusters of all negative emotions over all disaster periods. Socioeconomic status and infrastructural damage were predominantly correlated with disgust, fear, and shame post-disaster. We identified spatial clusters of emotional reactions during and in the aftermath of a large-scale disaster that could help provide guidance about where immediate and long-term relief measures are needed the most, if transferred to similar events and on comparable data worldwide.

History

School

  • Business and Economics

Department

  • Business

Published in

International Journal of Environmental Research and Public Health

Volume

18

Issue

10

Publisher

MDPI AG

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

11/05/2021

Publication date

2021-05-16

Copyright date

2021

eISSN

1660-4601

Language

en

Depositor

Dr Martin Sykora. Deposit date: 17 May 2021

Article number

5292