Abstract
Many business applications aim to take advantage of social media to target users and increase profit. To achieve this aim, it is necessary to understand the interests that drive users and the personal importance that they assign to each interest; the more importance a given interest has to a user, the more intense or relevant it is. While the end results are desirous, profiling users is a difficult task as users in general are not willing to explicitly reveal information about their interests. It is for this reason that interests must be inferred implicitly from their posts as understanding the users' most intense interests will help in the development of personalized recommendations and advertisements. The existing research in the domain focused on extracting user interests but none so far have considered intensity and the impact of time on expressed interests as factors. It is probable that the way in which a users' interest changes over time can be an ideal indicator of how much a given user likes a given topic. In this study, we propose a model to identify the users' interests and to rank them by importance by leveraging the content of tweets as well the time and frequency that a given user post tweets about their interests on social media.