Researches on recommending followees in social networks have attracted a lot of attentions in recent years. Existing studies on this topic mostly treat this kind of recommendation as just a type of friend recommendation. However, apart from making friends, the reason of a user to follow someone in social networks is inherently to satisfy his/her information needs in asymmetrical manner. In this paper, we propose a novel mining-based recommendation approach named Geographic-Textual-Social Based Followee Recommendation (GTS-FR), which takes into account the user movements, online texting and social properties to discover the relationship between users' information needs and provided information for followee recommendation. The core idea of our proposal is to discover users' similarity in terms of all the three properties of information which are provided by the users in a Location-Based Social Network (LBSN). To achieve this goal, we define three kinds of features to capture the key properties of users' interestingness from their provided information. In GTS-FR approach, we propose a series of novel similarity measurements to calculate similarity of each pair of users based on various properties. Based on the similarity, we make on-line recommendation for the followee a user might be interested in following. To our best knowledge, this is the first work on followee recommendation in LBSNs by exploring the geographic, textual and social properties simultaneously. Through a comprehensive evaluation using a real LBSN dataset, we show that the proposed GTS-FR approach delivers excellent performance and outperforms existing statofthe-art friend recommendation methods significantly.