Incorporating user interests evolution over time is a crucial problem in user profiling. We particularly focus on social profiling process that uses information shared on user social network to extract his/her interests. In this work, we apply our existing time-aware social profiling method on Twitter. The aim of this study is to measure the effectiveness of our approach on this kind of social network platform, which has different characteristics from those of other social networking sites.

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... ough the improvement compared to the time-agnostic baseline method is still low, the experiments using a parametric study showed us the benefit of applying a time-aware social profiling process on Twitter. We also found that our method performs well on sparse networks and that the information dynamic influences more the quality of our proposed time-aware method than the relationships dynamic while building the social profile on Twitter. This observation will lead us to a more complex study to find out meaningful factors to incorporate user interests evolution on social profiling process in such a network.