Incorporating user interest 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 have different characteristics from other social networking sites. Although the improvement compared to the time-agnostic baseline method is still low, the experiments using a parametric study enabled us to show the benefit of applying a time-aware social profiling process on Twitter. We also found that our method performs well on sparse networks. We also found that the information dynamic influenced 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 interest evolution on social profiling process in such a network.