Cargando…

Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study

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 applicati...

Descripción completa

Detalles Bibliográficos
Autores principales: Fung, Isaac Chun-Hai, Zeng, Jing, Chan, Chung-Hong, Liang, Hai, Yin, Jingjing, Liu, Zhaochong, Tse, Zion Tsz Ho, Fu, King-Wa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Australasian College for Infection Prevention and Control. Published by Elsevier B.V. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185480/
https://www.ncbi.nlm.nih.gov/pubmed/30479298
http://dx.doi.org/10.1016/j.idh.2017.08.005
_version_ 1783526765394657280
author Fung, Isaac Chun-Hai
Zeng, Jing
Chan, Chung-Hong
Liang, Hai
Yin, Jingjing
Liu, Zhaochong
Tse, Zion Tsz Ho
Fu, King-Wa
author_facet Fung, Isaac Chun-Hai
Zeng, Jing
Chan, Chung-Hong
Liang, Hai
Yin, Jingjing
Liu, Zhaochong
Tse, Zion Tsz Ho
Fu, King-Wa
author_sort Fung, Isaac Chun-Hai
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7185480
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Australasian College for Infection Prevention and Control. Published by Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-71854802020-04-28 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 Infect Dis Health Article 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. Australasian College for Infection Prevention and Control. Published by Elsevier B.V. 2018-03 2017-09-18 /pmc/articles/PMC7185480/ /pubmed/30479298 http://dx.doi.org/10.1016/j.idh.2017.08.005 Text en © 2017 Australasian College for Infection Prevention and Control. Published by Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Fung, Isaac Chun-Hai
Zeng, Jing
Chan, Chung-Hong
Liang, Hai
Yin, Jingjing
Liu, Zhaochong
Tse, Zion Tsz Ho
Fu, King-Wa
Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study
title Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study
title_full Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study
title_fullStr Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study
title_full_unstemmed Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study
title_short Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study
title_sort twitter and middle east respiratory syndrome, south korea, 2015: a multi-lingual study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185480/
https://www.ncbi.nlm.nih.gov/pubmed/30479298
http://dx.doi.org/10.1016/j.idh.2017.08.005
work_keys_str_mv AT fungisaacchunhai twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy
AT zengjing twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy
AT chanchunghong twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy
AT lianghai twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy
AT yinjingjing twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy
AT liuzhaochong twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy
AT tseziontszho twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy
AT fukingwa twitterandmiddleeastrespiratorysyndromesouthkorea2015amultilingualstudy