|
Twitter and Middle East respiratory syndrome, South Korea, 2015: a multi-lingual study
Fung, Isaac Chun-Hai
;
Zeng, Jing
;
Chan, Chung-hong
;
Liang, Hai
;
Yin, Jingjing
;
Liu, Zhaochong
;
Tse, Zion Tsz Ho
;
Fu, King-wa
|
DOI:
|
https://doi.org/10.1016/j.idh.2017.08.005
|
|
URL:
|
https://www.sciencedirect.com/science/article/pii/...
|
|
Additional URL:
|
https://www.idhjournal.com.au/article/S2468-0451(1...
|
|
Document Type:
|
Article
|
|
Year of publication:
|
2018
|
|
The title of a journal, publication series:
|
Infection, Health and Disease
|
|
Volume:
|
23
|
|
Issue number:
|
1
|
|
Page range:
|
10-16
|
|
Place of publication:
|
Amsterdam [u.a.]
|
|
Publishing house:
|
Elsevier
|
|
ISSN:
|
2468-0451
|
|
Publication language:
|
English
|
|
Institution:
|
School of Humanities > Medien I (Wessler)
Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
|
|
Subject:
|
300 Social sciences, sociology, anthropology
|
|
Abstract:
|
Background Different linguo-cultural communities might react to an outbreak differently. The 2015 South Korean MERS outbreak presented an opportunity for us to compare tweets responding to the same outbreak in different languages. Methods We obtained a 1% sample through Twitter streaming application programming interface from June 1 to 30, 2015. We identified MERS-related tweets with keywords such as ‘MERS’ and its translation in five different languages. We translated non-English tweets into English for statistical comparison. Results We retrieved MERS-related Twitter data in five languages: Korean (N = 21,823), English (N = 4024), Thai (N = 2084), Japanese (N = 1334) and Indonesian (N = 1256). Categories of randomly selected user profiles (p < 0.001) and the top 30 sources of retweets (p < 0.001) differed between the five language corpora. Among the randomly selected user profiles, K-pop fans ranged from 4% in the Korean corpus to 70% in the Thai corpus; media ranged from 0% (Thai) to 14% (Indonesian); political advocates ranged from 0% (Thai) to 19% (Japanese); medical professionals ranged from 0% (Thai) to 7% (English). Among the top 30 sources of retweets for each corpus (150 in total), 70 (46.7%) were media; 29 (19.3%) were K-pop fans; 7 (4.7%) were political; 9 (6%) were medical; and 35 (23.3%) were categorized as ‘Others’. We performed chi-square feature selection and identified the top 20 keywords that were most unique to each corpus. Conclusion Different linguo-cultural communities exist on Twitter and they might react to the same outbreak differently. Understanding audiences' unique Twitter cultures will allow public health agencies to develop appropriate Twitter health communication strategies.
|
 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Fung, Isaac Chun-Hai
;
Zeng, Jing
;
Chan, Chung-hong
;
Liang, Hai
;
Yin, Jingjing
;
Liu, Zhaochong
;
Tse, Zion Tsz Ho
;
Fu, King-wa
Google Scholar:
Fung, Isaac Chun-Hai
;
Zeng, Jing
;
Chan, Chung-hong
;
Liang, Hai
;
Yin, Jingjing
;
Liu, Zhaochong
;
Tse, Zion Tsz Ho
;
Fu, King-wa
ORCID:
Fung, Isaac Chun-Hai ; Zeng, Jing ; Chan, Chung-hong  ORCID: 0000-0002-6232-7530 ; Liang, Hai ; Yin, Jingjing ; Liu, Zhaochong ; Tse, Zion Tsz Ho ; Fu, King-wa ORCID: 0000-0001-8157-5276
You have found an error? Please let us know about your desired correction here: E-Mail
Actions (login required)
 |
Show item |
|
