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

• Main Authors: Annamaria Porreca, Francesca Scozzari, Marta Di Nicola
• Format: Article
• Language: English
• Published: BMC 2020-02-01
• Series: BMC Public Health
• Subjects: Vaccinations; YouTube; Sentiment analysis; Pro-vax; No-vax
• Online Access: http://link.springer.com/article/10.1186/s12889-020-8342-4

Abstract Background Recently, 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. Method Using co-occurrence network (CON) and sentiment analysis, we analysed the topics of YouTube Italian videos on vaccines in 2017 and 2018. Results The 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). Conclusion At 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.