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Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy
Social media have become a common way for people to express their personal viewpoints, including sentiments about health topics. We present the results of an opinion mining analysis on vaccination performed on Twitter from September 2016 to August 2017 in Italy. Vaccine-related tweets were automatic...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7227677/ https://www.ncbi.nlm.nih.gov/pubmed/32118519 http://dx.doi.org/10.1080/21645515.2020.1714311 |
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author | Tavoschi, Lara Quattrone, Filippo D’Andrea, Eleonora Ducange, Pietro Vabanesi, Marco Marcelloni, Francesco Lopalco, Pier Luigi |
author_facet | Tavoschi, Lara Quattrone, Filippo D’Andrea, Eleonora Ducange, Pietro Vabanesi, Marco Marcelloni, Francesco Lopalco, Pier Luigi |
author_sort | Tavoschi, Lara |
collection | PubMed |
description | Social media have become a common way for people to express their personal viewpoints, including sentiments about health topics. We present the results of an opinion mining analysis on vaccination performed on Twitter from September 2016 to August 2017 in Italy. Vaccine-related tweets were automatically classified as against, in favor or neutral in respect of the vaccination topic by means of supervised machine-learning techniques. During this period, we found an increasing trend in the number of tweets on this topic. According to the overall analysis by category, 60% of tweets were classified as neutral, 23% against vaccination, and 17% in favor of vaccination. Vaccine-related events appeared able to influence the number and the opinion polarity of tweets. In particular, the approval of the decree introducing mandatory immunization for selected childhood diseases produced a prominent effect in the social discussion in terms of number of tweets. Opinion mining analysis based on Twitter showed to be a potentially useful and timely sentinel system to assess the orientation of public opinion toward vaccination and, in future, it may effectively contribute to the development of appropriate communication and information strategies. |
format | Online Article Text |
id | pubmed-7227677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-72276772020-05-20 Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy Tavoschi, Lara Quattrone, Filippo D’Andrea, Eleonora Ducange, Pietro Vabanesi, Marco Marcelloni, Francesco Lopalco, Pier Luigi Hum Vaccin Immunother Research Paper Social media have become a common way for people to express their personal viewpoints, including sentiments about health topics. We present the results of an opinion mining analysis on vaccination performed on Twitter from September 2016 to August 2017 in Italy. Vaccine-related tweets were automatically classified as against, in favor or neutral in respect of the vaccination topic by means of supervised machine-learning techniques. During this period, we found an increasing trend in the number of tweets on this topic. According to the overall analysis by category, 60% of tweets were classified as neutral, 23% against vaccination, and 17% in favor of vaccination. Vaccine-related events appeared able to influence the number and the opinion polarity of tweets. In particular, the approval of the decree introducing mandatory immunization for selected childhood diseases produced a prominent effect in the social discussion in terms of number of tweets. Opinion mining analysis based on Twitter showed to be a potentially useful and timely sentinel system to assess the orientation of public opinion toward vaccination and, in future, it may effectively contribute to the development of appropriate communication and information strategies. Taylor & Francis 2020-03-02 /pmc/articles/PMC7227677/ /pubmed/32118519 http://dx.doi.org/10.1080/21645515.2020.1714311 Text en © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. |
spellingShingle | Research Paper Tavoschi, Lara Quattrone, Filippo D’Andrea, Eleonora Ducange, Pietro Vabanesi, Marco Marcelloni, Francesco Lopalco, Pier Luigi Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy |
title | Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy |
title_full | Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy |
title_fullStr | Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy |
title_full_unstemmed | Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy |
title_short | Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy |
title_sort | twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from september 2016 to august 2017 in italy |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7227677/ https://www.ncbi.nlm.nih.gov/pubmed/32118519 http://dx.doi.org/10.1080/21645515.2020.1714311 |
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