Online social networks provide valuable information sources to collect and identify user information and user interests. This work focuses on using information shared on users' egocentric network to extract user's interests. We propose to apply a time-aware method into an existing social profile building process, which is one of our previous team contributions. This strategy aims at weighting user's interests in the social profile according to their temporal relevance (temporal score). The temporal score of an interest is computed by combining the temporal score of information used to extract the interests (computed by taking into account their freshness) with the temporal score of individuals who share the information in the network (computed by taking into account the freshness of the interaction with the user). In this paper, we show results of intensive experiments conducted on scientific publication networks (DBLP/Mendeley) with presenting a parametric study, comparing the effectiveness of our technique with the time-agnostic technique. We study also the impact of the individual temporal score compared to the information temporal score. The experiments show that our proposition outperforms the existing time-agnostic egocentric network-based user profiling process in terms of precision and recall. Furthermore, we found that the individual temporal score has a larger importance than the information temporal score in calculating the final temporal score. This demonstrates that the dynamic links are more important than the dynamic information when using co-author network data to build the social profile.