ArticleBMC public health2020
Using text mining and sentiment analysis to analyse YouTube Italian videos concerning vaccination.
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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy.Computers in biology and medicine · 2021Pooled it
- Exploring Conversational Dynamics in Scientific and Pseudoscientific Health Communities on YouTube: Process Mining and Network Analysis Study.Journal of medical Internet research · 2026Article
- Collecting data from YouTube using the official API: A tutorial and a ready-to-use tool.Behavior research methods · 2026Article
- Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media.PloS one · 2024Article
- Spreading of misinformation on mass media and digital platforms regarding vaccines. A systematic scoping review on stakeholders, policymakers, and sentiments/behavior of Italian consumers.Human vaccines & immunotherapeutics · 2023Article
- Coordinated inauthentic behavior: An innovative manipulation tactic to amplify COVID-19 anti-vaccine communication outreachFrontiers in sociology · 2023Review
- On the development of an information system for monitoring user opinion and its role for the public.Journal of big data · 2022Article
- Effectiveness of Social Video Platforms in Promoting COVID-19 Vaccination Among Youth: A Content-Specific Analysis of COVID-19 Vaccination Topic Videos on Bilibili.Risk management and healthcare policy · 2022Article
- Changes of the Public Attitudes of China to Domestic COVID-19 Vaccination After the Vaccines Were Approved: A Semantic Network and Sentiment Analysis Based on Sina Weibo Texts.Frontiers in public health · 2021Article
- Article
- Media Data and Vaccine Hesitancy: Scoping Review.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.