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

Figures & data

Table 1. Examples of tweets included in the training set

Text of tweet – [English translation]	Classification label
“#NoVaccini #LibertaDiScelta. Un fondo per i danni da vaccini” – [“#NoVaccines #FreedomOfChoice. A fund for vaccine drawbacks”]	Against
“Ci ammalavamo una volta e ottenevamo l’immunita. Altro che vaccino! La libertà ai tempi del morbillo” – [“We got sick once and got immunity. We do not need vaccines! Freedom at the time of measles”]	Against
“Esiste una relazione chiarissima tra vaccini e l’autismo. Più vaccini, più i bambini sviluppano l’autismo, oltre ad altre malattie!” – [“There is a very clear relationship between vaccines and autism. The more vaccines, the more children develop autism, in addition to other diseases”]	Against
“Non vaccinare i propri figli è come circolare con un auto senza freni: un pericolo per tutti” – [“Not vaccinating your children is like traveling with a car without brakes: a danger for everyone”]	In favor
“I vaccini hanno superato tutti i test di efficacia e sicurezza. Non lasciamoci insinuare paure ingiustificate” – [“Vaccines have passed all the efficacy and safety tests. Let us not allow unjustified fears”]	In favor
“Mi raccomando non vaccinate i vostri figli, cosi potranno morire di morbillo!” – [“I recommend you do not vaccinate your children, so they can die of measles!”]	In favor
“Ma se fingessi di stare male dopo il vaccino per non andare a scuola??” – [“But what if I pretended to be sick after the vaccine for not going to school?”]	Neutral
“Altri casi di meningite registrati oggi . Guardate io non sono razzista. Ma troppe coincidenze non possono essere nemmeno! #BastaImmigrati” – [Other cases of meningitis recorded today. Look, I’m not a racist. But too many coincidences can not even be! #StopImmigration]	Neutral
“In Sicilia vaccino gratuito contro la meningite per i giovani” – [“In Sicily free vaccine against meningitis for young people”]	Neutral

Figure 1. Word cloud representation of tweets in the training dataset by class (A. in favor, B. against)

Figure 2. Number of tweets per month, total and by class (in favor, against, neutral), September 2016 – August 2017

Figure 3. Proportion of tweets by category (in favor, against, neutral) by month, September 2016 – August 2017

Figure 4. Analysis of neutrality and negative rates on vaccine-related events

Panel a. Publication of the National Plan for the Vaccine Prevention (PNPV) 2017–19 and agreement with Italian Regions for a vaccination-enforcing law (January 26^th, 2017); Panel b. Approval of the Legislative Decree n. 73 introducing 12 compulsory vaccinations (June 7^th, 2017); Panel c. Approval in the Italian Chamber of Deputies of the Vaccines Decree (July 28^th, 2017); Panel d. Approval of the law establishing vaccination requirements for school children in Emilia Romagna Region (November 22^nd, 2016); Panel e. News about the increase of 230% cases of measles in Italy (March 16^th, 2017). A two-sample test for equality of proportions, with Bonferroni correction for multiple comparisons, was performed. Adjusted p-value significance is shown (• <0.10, * <0.05, ** <0.01, *** <0.001). Comparisons are made with baseline (days −5 to −1). Error bars show 95% binomial confidence intervals for proportions.

Table 2. Accuracy of the monitoring tool for single events

Event*	Accuracy (%)
A	61.9
B	61.6
C	62.4
D	62.1
E	64.7
Average Accuracy	62.1

*Letters refer to the same events represented in Figure 4