Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example. Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media. This final work aims at analyzing the relationship between personality and different types of social networks users, including popular users and influentials. The work will be based on the Big Five personality model that states that an individual is associated with five scores that correspond to the five main personality traits and that form the acronym of OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism). The project will be based on an existing dataset that collects activity on social networks (postings, friends, network size) and will the methodology of machine learning projects. The main goal of the project is to create a personality traits classifier, in such a way given a social network user, it responds with the score in each of the five traits. To achieve this purpose, the project includes a first phase of learning the most popular machine learning algorithms and reading several articles to know how the social interaction is related to personality. In addition, during the first phase, the student will learn in depth Python as well as machine learning libraries (scikit-learn and pandas) and natural language ones (such as NLTK). Then the project will follow a second phase of data analysis and application of machine learning techniques and will conclude with the writing of the book.