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Erschienen in: Social Network Analysis and Mining 1/2019

01.12.2019 | Original Article

Social BI to understand the debate on vaccines on the Web and social media: unraveling the anti-, free, and pro-vax communities in Italy

verfasst von: Matteo Francia, Enrico Gallinucci, Matteo Golfarelli

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2019

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Abstract

The debate on vaccines in Italy has greatly intensified in recent years. The promulgation of a law that makes a set of ten vaccines obligatory has pushed this formerly niche topic to a mainstream level. The law itself is an answer to the progressive erosion of the vaccine coverage. The debate has become a political topic with three main positions: supporters of the importance of vaccines, opponents who claim that vaccines are harmful to health, and the new position of those contesting only the mandatoriness of vaccinations. In this paper, we build on a Social Business Intelligence architecture to propose an in-depth analysis of the emerging social debate. Our analysis spans over more than three years, covering all the Web and social media. We adopt several techniques, including community detection and text analytics, to understand the evolution of the debate, the discussed topics, and the structure and peculiarities of the main social communities. The study reveals that the communities are well characterized, especially from a political perspective, and provides useful insights to official health organizations to improve their communication strategies.

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Fußnoten
1
Preliminary results on the first years after the promulgation of the law show an increase in vaccination coverage, suggesting a positive influence of said regulation (D’Ancona et al. 2018; Signorelli et al. 2018)
 
2
In November 2017, the Italian Constitutional Court has ruled in favor of the law on mandatory vaccinations, declaring it to be legitimate (Petrarca et al. 2018; Ministero della Salute 2017).
 
3
Whereas no-vax users are also against the obligation, we distinguish free-vax users for being either in favor of or neutral to vaccinations.
 
4
Political orientation has been identified through a manual approach: for public figures with verified accounts, we used the declared profile; for other, users we browsed their tweets and inferred the political orientation from their content.
 
5
Modularity ranges from − 1 to 1, where a positive number indicates a dense network.
 
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Metadaten
Titel
Social BI to understand the debate on vaccines on the Web and social media: unraveling the anti-, free, and pro-vax communities in Italy
verfasst von
Matteo Francia
Enrico Gallinucci
Matteo Golfarelli
Publikationsdatum
01.12.2019
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2019
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-019-0590-x

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