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Description

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.

Learning Objective 1: Utilize a mixed methods approach, in addition to machine learning, to monitor and assess the dissemination of health information (e.g. that related to Zika) on social media

Learning Objective 2: Formulate effective strategies for communicating public health information on social media

Learning Objective 3: Identify opportunities and challenges that social media present to risk communication

Authors:

Xinning Gui (Presenter)
University of California, Irvine

Yue Wang (Presenter)
University of Michigan

Yubo Kou, Purdue University
Tera Reynolds, University of California, Irvine
Yunan Chen, University of California, Irvine
Qiaozhu Mei, University of Michigan
Kai Zheng, University of California, Irvine

Presentation Materials:

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