A Comparative Analysis of Social Media Content on Migration in Italy and the United States

This study analyzes user-generated content on social media regarding migration, focusing on tweets from Italy and the United States. Using a BERT-based model, we analyzed approximately 24,000 tweets to explore differences in public perception, focusing mainly on sentiment and emotion analysis. The results reveal that Italian tweets tend to express neutral sentiment accompanied by anger, while tweets from the United States generally reflect more negative sentiment, especially fear. Hashtag analysis highlights the influence of political events on discussions. Additionally, Latent Dirichlet Allocation identified key discussion topics, and fake news detection revealed that most shared content was reliable. This research provides insights into regional differences in migration views on social media.