- Alice Pagano (University of Padova)
- Rachele Favaretto (University of Padova)
- Alessandro Lambertini (University of Padova)
Vaccines are a central topic in public debate and the spreading of novax opinions on social networks have a relevant influence in vaccination hesitancy. In this context, a quantitative modelling of this phenomenon could help to organize more effective and less dispersive national vaccination plans. In this work, we develop a method to extract a spatial distribution of novax opinions from Twitter. In particular, we exploit it to analyze anti-vaccine opinions distribution in Italy in the time window spacing from 2017 to 2020 at a region level. We observe the impact on Twitter activity of the Covid-19 pandemic. Moreover, for a fixed year, we investigate a possible correlation between novax ideas distribution and measles vaccination coverage. However, due to poor statistic, we observe no correspondence between low vaccination coverage and novax hubs. Nonetheless, mining and localizing opinions from social networks could have a great relevance, because it can be useful in identifying hubs of anti-vaccination ideas and consequently in choosing where to invest resources for public opinion awareness campaigns on vaccination.
This project is organized in multiple directories as:
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Code: this folder contains the jupyter notebook for performing the analysis and the data
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Paper: this folder contains the latex report
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Presentation: this folder contains the beamer presentation