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

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Listed:
  • Oliver Gruebner

    (Department of Geography, Humboldt-Universität zu Berlin, Berlin 10099, Germany
    Epidemiology, Biostatistics, and Prevention Institute (EBPI), University of Zurich, 8001 Zurich, Switzerland)

  • 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)

  • SV Subramanian

    (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)

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.

Suggested Citation

  • Oliver Gruebner & Sarah R. Lowe & Martin Sykora & Ketan Shankardass & SV Subramanian & Sandro Galea, 2018. "Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media," IJERPH, MDPI, vol. 15(10), pages 1-12, October.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2275-:d:176205
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    Citations

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    Cited by:

    1. Lauren A. Clay & Ashley D. Ross, 2020. "Factors Associated with Food Insecurity Following Hurricane Harvey in Texas," IJERPH, MDPI, vol. 17(3), pages 1-17, January.
    2. Yeqing Cheng & Yan Chen & Bing Xue & Jinping Zhang, 2021. "Regional Differentiation and Influencing Factor Analysis of Residents’ Psychological Status during the Early Stage of the COVID-19 Pandemic in South China," IJERPH, MDPI, vol. 18(22), pages 1-19, November.
    3. Joel Oommen George & Suzanne Elayan & Martin Sykora & Marin Solter & Rob Feick & Christopher Hewitt & Yiqiao Liu & Ketan Shankardass, 2023. "The Role of Social Media in Building Pandemic Resilience in an Urban Community: A Qualitative Case Study," IJERPH, MDPI, vol. 20(17), pages 1-18, September.
    4. Xuehua Han & Juanle Wang & Min Zhang & Xiaojie Wang, 2020. "Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China," IJERPH, MDPI, vol. 17(8), pages 1-22, April.
    5. Shi Shen & Ke Shi & Junwang Huang & Changxiu Cheng & Min Zhao, 2023. "Global online social response to a natural disaster and its influencing factors: a case study of Typhoon Haiyan," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    6. Jiangmei Xiong & Yulin Hswen & John A. Naslund, 2020. "Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
    7. Seungil Yum, 2022. "Social networks and spatial-temporal analyses for winter storm Jupiter in the US in 2017," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2091-2105, August.
    8. Sonja I. Garske & Suzanne Elayan & Martin Sykora & Tamar Edry & Linus B. Grabenhenrich & Sandro Galea & Sarah R. Lowe & Oliver Gruebner, 2021. "Space-Time Dependence of Emotions on Twitter after a Natural Disaster," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    9. Emily Ying Yang Chan & Holly Ching Yu Lam, 2020. "Research Frontiers of Health Emergency and Disaster Risk Management: What Do We Know So Far?," IJERPH, MDPI, vol. 17(5), pages 1-4, March.
    10. Meijie Chu & Wentao Song & Zeyu Zhao & Tianmu Chen & Yi-chen Chiang, 2024. "Emotional contagion on social media and the simulation of intervention strategies after a disaster event: a modeling study," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    11. Dajun Dai & Ruixue Wang, 2020. "Space-Time Surveillance of Negative Emotions after Consecutive Terrorist Attacks in London," IJERPH, MDPI, vol. 17(11), pages 1-15, June.

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