Understanding the Patterns of Health Information Dissemination on Social Media during the Zika Outbreak. Gui, X., Wang, Y., Kou, Y., Reynolds, T. L., Chen, Y., Mei, Q., & Zheng, K. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017:820–829, 2017. abstract bibtex Social media are important platforms for risk communication during public health crises. Effective dissemination of accurate, relevant, and up-to-date health information is important for the public to raise awareness and develop risk management strategies. This study investigates Zika virus-related information circulated on Twitter, identifying the patterns of dissemination of popular tweets and tweets from public health authorities such as the CDC. We leveraged a large corpus of Twitter data covering the entire year of 2016. We analyzed the data using quantitative and qualitative content analyses, followed by machine learning to scale the manual content analyses to the corpus. The results revealed possible discrepancies between what the general public was most interested in, or concerned about, and what public health authorities provided during the Zika outbreak. We provide implications for public health authorities to improve risk communication through better alignment with the general public's information needs during public health crises.
@article{gui_understanding_2017,
title = {Understanding the {Patterns} of {Health} {Information} {Dissemination} on {Social} {Media} during the {Zika} {Outbreak}},
volume = {2017},
issn = {1942-597X},
abstract = {Social media are important platforms for risk communication during public health crises. Effective dissemination of accurate, relevant, and up-to-date health information is important for the public to raise awareness and develop risk management strategies. This study investigates Zika virus-related information circulated on Twitter, identifying the patterns of dissemination of popular tweets and tweets from public health authorities such as the CDC. We leveraged a large corpus of Twitter data covering the entire year of 2016. We analyzed the data using quantitative and qualitative content analyses, followed by machine learning to scale the manual content analyses to the corpus. The results revealed possible discrepancies between what the general public was most interested in, or concerned about, and what public health authorities provided during the Zika outbreak. We provide implications for public health authorities to improve risk communication through better alignment with the general public's information needs during public health crises.},
language = {eng},
journal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},
author = {Gui, Xinning and Wang, Yue and Kou, Yubo and Reynolds, Tera Leigh and Chen, Yunan and Mei, Qiaozhu and Zheng, Kai},
year = {2017},
pmid = {29854148},
pmcid = {PMC5977662},
keywords = {Communication, Consumer Health Information, Disease Outbreaks, Humans, Information Dissemination, Machine Learning, Public Health Practice, Risk, Social Media, Zika Virus, Zika Virus Infection},
pages = {820--829}
}
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