Evidence map›Paper›PMID 34739459›Full record

ArticleActa bio-medica : Atenei Parmensis2021

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

Open access · greenAbstract read
In one paragraph

Article in Acta bio-medica : Atenei Parmensis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Davide GoriDepartment of Biomedical and Neuromotor Sciences, University of Bologna. davide.gori4@unibo.it.
Francesco DurazziDepartment of Physics, University of Bologna. francesco.durazzi2@unibo.it.
Marco Montalti. marco.montalti7@studio.unibo.it.
Zeno Di ValerioDepartment of Biomedical and Neuromotor Sciences, University of Bologna. zeno.divalerio@studio.unibo.it.
Chiara RenoDepartment of Biomedical and Neuromotor Sciences, University of Bologna. chiara.reno@studio.unibo.it.
Maria Pia FantiniDepartment of Biomedical and Neuromotor Sciences, University of Bologna. mariapia.fantini@unibo.it.
Daniel RemondiniDepartment of Physics, University of Bologna. daniel.remondini@unibo.it.
University of Bologna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimA 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.

methodsTweets 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.

resultsBased 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.

Indexed as

COVID-19Social MediaVaccinesCommunicationHumansItalyPandemicsSARS-CoV-2Vaccines

Identifiers

PMID34739459
PMCPMC8851020
OpenAlexW3211023713

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.