The amount of information generated by modern societies is constantly growing. The data that can be extracted about news, users, or trends are practically infinite. Nowadays, many companies are aware of this phenomenon and leverage the power of analytical tools to obtain key data about people’s behavior. In my personal project, the Formula 1 news platform called ”JaramaFan” has a large following on social media, especially on the Twitter platform. Therefore, we will use the data from this account to carry out the research for this project. We will focus on network analysis using graph theory to create a network map, which will be studied subsequently. We will identify the main communities within the global network, specifically the Formula 1 community, and analyze the popularity metrics for the involved accounts. Additionally, we will employ Natural Language Processing (NLP) to analyze the emotions and sentiments expressed in the tweets from these accounts. The ultimate goal of this work is to correlate the data obtained, both in terms of popularity metrics and sentiment analysis. This will provide insight into a better understanding of social networks. At the end of the analysis, we will understand how emotions influence tweet popularity, how communities are formed on Twitter, and how popularity metrics vary among different accounts.