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The dramatic increase in anti-vaccine discourses during the COVID-19 pandemic: a social network analysis of Twitter.
Durmaz, Nihal; Hengirmen, Engin.
  • Durmaz N; Department of Pediatrics, Gülhane Training and Research Hospital, Ankara, Turkey.
  • Hengirmen E; Management Information Systems, Fenerbahce University, Istanbul, Turkey.
Hum Vaccin Immunother ; 18(1): 2025008, 2022 12 31.
Article in English | MEDLINE | ID: covidwho-1672020
ABSTRACT
BACKGROUND/

AIM:

The first case of COVID-19 in Turkey was officially recorded on March 11, 2020. Social media use increased worldwide, as well as in Turkey, during the pandemic, and conspiracy theories/fake news about medical complications of vaccines spread throughout the world. The aim of this study was to identify community interactions related to vaccines and to identify key influences/influencers before and after the pandemic using social network data from Twitter. MATERIALS AND

METHODS:

Two datasets, including tweets about vaccinations before and after COVID-19 in Turkey, were collected. Social networks were created based on interactions (mentions) between Twitter users. Users and their influence were scored based on social network analysis and parameters that included in-degree and betweenness centrality.

RESULTS:

In the pre-COVID-19 network, media figures and authors who had anti-vaccine views were the most influential users. In the post-COVID-19 network, the Turkish minister of health, the was the most influential figure. The vaccine network was observed to be growing rapidly after COVID-19, and the physicians and authors who had opinions about mandatory vaccinations received a great deal of reaction. One-way communication between influencers and other users in the network was determined.

CONCLUSIONS:

This study shows the effectiveness and usefulness of large social media data for understanding public opinion on public health and vaccination in Turkey. The current study was completed before the implementation of the COVID-19 vaccine in Turkey. We anticipated that social network analysis would help reduce the "infodemic" before administering the vaccine and would also help public health workers act more proactively in this regard.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / Social Media / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Long Covid / Vaccines Limits: Humans Language: English Journal: Hum Vaccin Immunother Year: 2022 Document Type: Article Affiliation country: 21645515.2021.2025008

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / Social Media / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Long Covid / Vaccines Limits: Humans Language: English Journal: Hum Vaccin Immunother Year: 2022 Document Type: Article Affiliation country: 21645515.2021.2025008