Communicating COVID-19: Twitter Surveillance and Affect in Relation to Regional Differences in Italy

The coronavirus disease (Covid-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. This study explores how people access information on Covid-19 via social networks. The aim of this study is to analyze sentiment discussions on Twitter related to the Covid-19 outbreak. This study applied machine learning methods in the field of artificial intelligence to analyze Italy’s data collected from Twitter in Italy’s major metropolitan city in the north region of Italy (Lombardy). Using tweets originating exclusively in Italy during the months of March-June, 2020, the study examined Covid-19 related sentiment discussions. Social network and sentiment analysis were also conducted to determine the social network of dominant topics and whether the tweets expressed fear, anger, and joy sentiments. The study employed a Pearson's Correlation Coefficient to identify the most common measures between indicators. The study analyzed and collected a total of 1,372,402 tweets in the Metropolitan City of Milan, Lombardy, Italy during the study timeframe. The study identified dominant influential policy Covid-19 related sentiment tweets (Phase 0, Phase I, Phase II, and Phase III), a total number of Covid deaths, and a total number of Covid cases, protesting racism, emotional support, business economy, social change, and psychological stress.

Keywords: Sentiment analysis; Italy; COVID-19; Social media; Twitter

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