In this paper, we propose a semi-supervised approach to gender classification by leveraging textual features and a specific kind of indirect links among the users which we call "same-interest" links.
we propose a factor graph, namely Textual and Social Factor Graph (TSFG), to model both the textual and the "same-interest" link information.