Monitoring User Evolution in Twitter

Nowadays social media are widely used for the broadcasting of different types of information, such as events, activities and opinions. Analyzing this vast amount of data for extracting models that describe individual users or groups of users has gained a lot of attention lately. In this work we analyze individual users and monitor changes in their published content over time. We propose a topic-based user profiling and monitoring approach for change detection and monitoring of profile evolution. Our method is capable of detecting persistent topics representing long term interests of the user as well as short term topics that refer to everyday events or reactions to the news. We evaluate our approach on real data from Twitter.