Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy

Abstract

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.

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

“…Basic information about Twitter and the definitions of the Twitter terms mentioned in this article can be found in Appendix A . Published articles about opinion analysis towards vaccination on social media usually perform short-term SA [ 9 , 24 , 25 ] in small datasets [ 24 , 26 ]. Some studies are limited to a single location [ 24 , 25 , 27 ], but usually geolocation is not analysed [ 3 ].…”

“…Published articles about opinion analysis towards vaccination on social media usually perform short-term SA [ 9 , 24 , 25 ] in small datasets [ 24 , 26 ]. Some studies are limited to a single location [ 24 , 25 , 27 ], but usually geolocation is not analysed [ 3 ]. In our research, we aim to evaluate public perceptions regarding vaccination on Twitter by performing a sentence-level SA on a dataset composed of 1,499,227 vaccine-related tweets, in English and Spanish, published from June 2011 to April 2019.…”

Feeling the Void: Lack of Support for Isolation and Sleep Difficulties in Pregnant Women during the COVID-19 Pandemic Revealed by Twitter Data Analysis

“…Thus, Twitter can be used as a tool to assess the general state of events, such as the COVID-19 pandemic and its effect on the mental health of pregnant women and mothers [ 29 ]. There are multiple approaches to gather and analyze data from Twitter according to the research subject and keywords [ 30 , 31 ]. Some approaches utilize supervised machine learning to limit human error in finding tweets of interest [ 30 ].…”

“…There are multiple approaches to gather and analyze data from Twitter according to the research subject and keywords [ 30 , 31 ]. Some approaches utilize supervised machine learning to limit human error in finding tweets of interest [ 30 ]. Other approaches use automated data analysis and clustering techniques that allows for easy and efficient subject grouping [ 31 ].…”

An exploratory analysis of public opinion and sentiments towards COVID-19 pandemic using Twitter data.

“…Twitter has been increasingly used as a data source for health-related research, offering a more e cient means of data collection over traditional survey methods [2].For instance, it has been used to examine disease stigma [3], and to monitor disease pandemics [4] [5]. More recent studies have used Twitter data for the assessment of public sentiments, attitudes and opinions about health related issues [6] [7]. These studies have shown that there may be scope for using Twitter generated data to provide insight about the public opinion on the current COVID-19 pandemic.…”