During the COVID-19 pandemic, vaccination emerged as a burning issue in the Italian public discussion. In particular, social media were an important vehicle for spreading news, information, and opinions, both true and false, regarding health. In this contribution, we present an application of Structural Topic Model (STM) to a tweets-based corpus concerning the Italian public debate about COVID-19 vaccines. The aim is to detect the evolution of tweets-related topics characterizing the Italian public opinion about COVID-19 vaccination.
Twitting about COVID-19: An application of Structural Topic Models to a sample of Italian tweets
Antonio Calcagnì;Livio FInos
2022
Abstract
During the COVID-19 pandemic, vaccination emerged as a burning issue in the Italian public discussion. In particular, social media were an important vehicle for spreading news, information, and opinions, both true and false, regarding health. In this contribution, we present an application of Structural Topic Model (STM) to a tweets-based corpus concerning the Italian public debate about COVID-19 vaccines. The aim is to detect the evolution of tweets-related topics characterizing the Italian public opinion about COVID-19 vaccination.
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