Abstract

This study provides a detailed analysis of COVID-19-related social media discourse in Italy, using 535,886 tweets from 10 major cities between August 30, 2020, and June 8, 2021. Tweets were translated from Italian to English for analysis. A multi-faceted methodology was employed: Latent Dirichlet Allocation (LDA) identified 20 key themes; sentiment analysis, using TextBlob, Flair and TweetNLP, and emotion recognition using TweetNLP, revealed the emotional tone of the discourse, with 453 tweets unanimously positive across all algorithms. TextBlob was used for lexical analysis to rank the most salient positive and negative terms. Results indicated that positive sentiments centered on hope, safety measures, and vaccination progress, while negative sentiments focused on fear, death, and quarantine frustrations. This research offers valuable insights for public health officials, enabling tailored messaging, real-time strategy monitoring, and agile policymaking during the pandemic, with implications for future health crises.

Keywords: Covid-19 Social Media Analysis, Sentiment Analysis, Topic Modeling, Public Health Communication, Italian Twitter Discourse 

Tools Used

• Python libraries - numpy, pandas, sklearn, seaborn, matplotlib, transformer models

• Microsoft Excel - Data Processing