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Analyzing the changing landscape of the Covid-19 vaccine debate on Twitter

Santoro, Arnaldo
•
Galeazzi, Alessandro
•
Scantamburlo, Teresa
altro
Zollo, Fabiana
2023
  • journal article

Periodico
SOCIAL NETWORK ANALYSIS AND MINING
Abstract
The issue of vaccine hesitancy has posed a significant challenge during the Covid-19 pandemic, as it increases the risk of undermining public health interventions aimed at mitigating the spread of the virus. While the swift development of vaccines represents a remarkable scientific achievement, it has also contributed to skepticism and apprehension among some populations. Against this backdrop, the suspension of the AstraZeneca vaccine by the European Medicines Agency further exacerbated an already contentious debate around vaccine safety. This paper examines the Twitter discourse surrounding Covid-19 vaccines, focusing on the temporal and geographical dimensions of the discussion. Using over a year’s worth of data, we study the public debate in five countries (Germany, France, UK, Italy, and the USA), revealing differences in the interaction structure and in the production volume of questionable and reliable sources. Topic modeling highlights variations in the perspectives of reliable and questionable sources, but some similarities across nations. Also, we quantify the effect of vaccine announcement and suspension, finding that only the former had a significant impact in all countries. Finally, we analyze the evolution of the communities in the interaction network, revealing a relatively stable scenario with a few considerable shifts between communities with different levels of reliability. Our results suggest that major external events can be associated with changes in the online debate in terms of content production and interaction patterns. However, despite the AZ suspension, we do not observe any noticeable changes in the production and consumption of misinformation related to Covid-19 vaccines.
DOI
10.1007/s13278-023-01127-3
WOS
WOS:001066516000002
Archivio
https://hdl.handle.net/11368/3117688
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85171477190
https://link.springer.com/article/10.1007/s13278-023-01127-3
https://ricerca.unityfvg.it/handle/11368/3117688
Diritti
open access
license:creative commons
license uri:http://creativecommons.org/licenses/by/4.0/
FVG url
https://arts.units.it/bitstream/11368/3117688/2/s13278-023-01127-3.pdf
Soggetti
  • Covid-19

  • Infodemic

  • Misinformation

  • Social media

  • Twitter

  • Vaccine

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