Citation

Woo H, Cho Y, Shim E, Lee K, Song G. Int. J. Environ. Res. Public Health 2015; 12(9): 10974-10983.

Affiliation

Mining Laboratory, Daumsoft, Seoul 140-887, Korea. kysong@daumsoft.com.

Copyright

(Copyright © 2015, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph120910974

PMID

26404349

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.

Language: en