Evidence map›Paper›PMID 32075631›Full record

ArticleBMC public health2020

Using text mining and sentiment analysis to analyse YouTube Italian videos concerning vaccination.

Annamaria Porreca, Francesca Scozzari, Marta Di Nicola

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

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

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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

3 authors.

Annamaria PorrecaDepartment of Economic Studies, G. d'Annunzio University of Chieti-Pescara, Viale della Pineta 4, Pescara, Italy. annamaria.porreca@unich.it.ORCID http://orcid.org/0000-0003-3278-1561
Francesca ScozzariDepartment of Economic Studies, G. d'Annunzio University of Chieti-Pescara, Viale della Pineta 4, Pescara, Italy.
Marta Di NicolaDepartment of Medical Oral Science and Biotechnology, G. d'Annunzio University of Chieti-Pescara Chieti, Via dei Vestini 31, Chieti, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently, social networks have become a popular source of information on health topics. Particularly, in Italy, there is a lively discussion on the web regarding vaccines also because there is low vaccination coverage, vaccines hesitancy, and anti-vaccine movements. For these reasons, in 2017, Institutions have introduced a law to force children to make ten compulsory vaccines for school attendance and proposed a vaccination campaign. On social networks, this law has fostered a fierce discussion between pro-vaccinations and anti-vaccinations people. This paper aims to understand if and how the population's opinion has changed before the law and after the vaccination campaign using the titles of the videos uploaded on Youtube in these periods.

methodUsing co-occurrence network (CON) and sentiment analysis, we analysed the topics of YouTube Italian videos on vaccines in 2017 and 2018.

resultsThe CON confirms that vaccinations were very disapproved before the law. Instead, after the communication campaign, people start to be less critical. The sentiment analysis shows that the intense vaccination campaign also promoted by medical doctors pushed the sentiment to change polarity from a prevailing negative opinion in 2017 (52% negative) to a positive one in 2018 (54% positive).

conclusionAt the population level, the potential misinformation of social networks could be significant and is a real risk for health. Our study highlights that vaccination campaigns on social networks could be an essential instrument of health policies and a sharp weapon to fight ignorance and misrepresentations of non-qualified people influencing individuals' decision-making.

Indexed as

Attitude to HealthHealth CommunicationSocial MediaChildData MiningHumansImmunization ProgramsItalySchoolsVaccinationVideotape RecordingNo-vaxPro-vaxSentiment analysisVaccinationsYouTube

Identifiers

PMID32075631
PMCPMC7031868

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.