Abstract
Analysis and mining of social media has become an important research area. A challenging problem in this area consists in the identification of a group of users with similar patterns. In this paper, we propose the classification of users based on their activity profiles (e.g., periods of the day when the user is most and least active in online communications). Activity profiles can be useful for many purposes, such as marketing and user behavior analysis. They can also serve as a basis for other techniques such as stylometric and time analysis in order to increase the precision and scalability of multiple aliases identification techniques. We have implemented a prototype tool and applied it on a dataset from the ICWSM data set Boards.ie, showing the usefulness of our classification.