ArticleInfection, disease & health2018
Twitter and Middle East respiratory syndrome, South Korea, 2015: A multi-lingual study.
Article in Infection, disease & health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Social media based surveillance systems for healthcare using machine learning: A systematic review.Journal of biomedical informatics · 2020Pooled it
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Place branding: Religion in shaping the three-dimensional essence of a city brand through stakeholder engagement.PloS one · 2024Article
- Winter Storms and Unplanned School Closure Announcements on Twitter: Comparison Between the States of Massachusetts and Georgia, 2017-2018.Disaster medicine and public health preparedness · 2022Article
- Census of Twitter users: Scraping and describing the national network of South Korea.PloS one · 2022Article
- Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak.Social network analysis and mining · 2021Article
- Achievements and highlights for Infection, Disease and Health.Infection, disease & health · 2019Article
- Twitter Conversations and English News Media Reports on Poliomyelitis in Five Different Countries, January 2014 to April 2015.The Permanente journal · 2019Article
- #CDCGrandRounds and #VitalSigns: A Twitter Analysis.Annals of global health · 2018Article
- Contents, Followers, and Retweets of the Centers for Disease Control and Prevention's Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study.JMIR public health and surveillance · 2018Article
- Sentiment, Contents, and Retweets: A Study of Two Vaccine-Related Twitter Datasets.The Permanente journal · 2018Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferent 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.
methodsWe 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.
resultsWe 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.
conclusionDifferent 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.