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Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood

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  • Hyekyung Woo

    (School of Public Health, Seoul National University, Seoul 151-742, Korea
    Institute of Health and Environment, Seoul National University, Seoul 151-742, Korea)

  • Youngtae Cho

    (School of Public Health, Seoul National University, Seoul 151-742, Korea
    Institute of Health and Environment, Seoul National University, Seoul 151-742, Korea)

  • Eunyoung Shim

    (School of Public Health, Seoul National University, Seoul 151-742, Korea
    Institute of Health and Environment, Seoul National University, Seoul 151-742, Korea)

  • Kihwang Lee

    (Mining Laboratory, Daumsoft, Seoul 140-887, Korea)

  • Gilyoung Song

    (Mining Laboratory, Daumsoft, Seoul 140-887, Korea)

Abstract

The Sewol ferry disaster severely shocked Korean society. The objective of this study was to explore how the public mood in Korea changed following the Sewol disaster using Twitter data. Data were collected from daily Twitter posts from 1 January 2011 to 31 December 2013 and from 1 March 2014 to 30 June 2014 using natural language-processing and text-mining technologies. We investigated the emotional utterances in reaction to the disaster by analyzing the appearance of keywords, the human-made disaster-related keywords and suicide-related keywords. This disaster elicited immediate emotional reactions from the public, including anger directed at various social and political events occurring in the aftermath of the disaster. We also found that although the frequency of Twitter keywords fluctuated greatly during the month after the Sewol disaster, keywords associated with suicide were common in the general population. Policy makers should recognize that both those directly affected and the general public still suffers from the effects of this traumatic event and its aftermath. The mood changes experienced by the general population should be monitored after a disaster, and social media data can be useful for this purpose.

Suggested Citation

  • Hyekyung Woo & Youngtae Cho & Eunyoung Shim & Kihwang Lee & Gilyoung Song, 2015. "Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood," IJERPH, MDPI, vol. 12(9), pages 1-10, September.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:9:p:10974-10983:d:55283
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    References listed on IDEAS

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    1. Hong-Hee Won & Woojae Myung & Gil-Young Song & Won-Hee Lee & Jong-Won Kim & Bernard J Carroll & Doh Kwan Kim, 2013. "Predicting National Suicide Numbers with Social Media Data," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-6, April.
    2. Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    3. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
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    Cited by:

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    2. Marco Palomino & Tim Taylor & Ayse Göker & John Isaacs & Sara Warber, 2016. "The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”," IJERPH, MDPI, vol. 13(1), pages 1-23, January.
    3. Rebecca A. Bernert & Amanda M. Hilberg & Ruth Melia & Jane Paik Kim & Nigam H. Shah & Freddy Abnousi, 2020. "Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations," IJERPH, MDPI, vol. 17(16), pages 1-25, August.
    4. Xiaodong Cao & Piers MacNaughton & Zhengyi Deng & Jie Yin & Xi Zhang & Joseph G. Allen, 2018. "Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA," IJERPH, MDPI, vol. 15(2), pages 1-15, February.

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