Mis-tweeting communication: a Vaccine Hesitancy analysis among twitter users in Italy

Davide Gori; Francesco Durazzi; Marco Montalti; Zeno Di Valerio; Chiara Reno; Maria Pia Fantini; Daniel Remondini

doi:10.23750/abm.v92iS6.12251

Mis-tweeting communication: a Vaccine Hesitancy analysis among twitter users in Italy

Authors

Davide Gori Department of Biomedical and Neuromotor Sciences, University of Bologna https://orcid.org/0000-0003-4954-9419
Francesco Durazzi Department of Physics, University of Bologna https://orcid.org/0000-0001-8729-6438
Marco Montalti https://orcid.org/0000-0001-7276-0277
Zeno Di Valerio Department of Biomedical and Neuromotor Sciences, University of Bologna https://orcid.org/0000-0002-2422-5699
Chiara Reno Department of Biomedical and Neuromotor Sciences, University of Bologna https://orcid.org/0000-0002-7933-2141
Maria Pia Fantini Department of Biomedical and Neuromotor Sciences, University of Bologna https://orcid.org/0000-0002-3257-6552
Daniel Remondini Department of Physics, University of Bologna https://orcid.org/0000-0003-3185-7456

DOI: https://doi.org/10.23750/abm.v92iS6.12251

Keywords:

Vaccine Hesitancy, Twitter, Social Media, Public Health, Vaccination, COVID-19, SARS-CoV-2, anti-vax

Abstract

Background and aim: A previously unseen body of scientific knowledge of varying quality has been produced during the ongoing COVID-19 pandemic. It has proven extremely difficult to navigate for experts and laymen alike, originating a phenomenon described as “Infodemic”, a breeding ground for misinformation. This has a potential impact on vaccine hesitancy that must be considered in a situation where efficient vaccination campaigns are of the greatest importance. We aimed at describing the polarization and volumes of Italian language tweets in the months before and after the start of the vaccination campaign in Italy.

Methods: Tweets were sampled in the October 2020-January 2021 period. The characteristics of the dataset were analyzed after manual annotation as Anti-Vax, Pro-Vax and Neutral, which allowed for the definition of a polarity score for each tweet.

Results: Based on the annotated tweets, we could identify 29.6% of the 2,538 unique users as anti-Vax and 12.1% as pro-Vax, with a strong disagreement in annotation in 7.1% of the tweets. We observed a change in the proportion of retweets to anti-Vax and pro-Vax messages after the start of the vaccination campaign in Italy. Although the most shared tweets are those of opposite orientation, the most retweeted users are moderately polarized.

Conclusions: The disagreement on the manual classification of tweets highlights a potential risk for misinterpretation of tweets among the general population. Our study reinforces the need to focus Public Health’s attention on the new social media with the aim of increasing vaccine confidence, especially in the context of the current pandemic.

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