Online Social Network User Home Location Inference Based on Heterogeneous Networks

Inferring the home locations of online social network (OSN) users from their corresponding account data is an important process for many applications, such as personal privacy protection and business advertising applications. The existing methods typically use a supervised learning method to infer a user's home location according to a single or partial aspect of their OSN information. However, the home location of a user may be represented in a biased way if only a single or partial aspect of the information is used, and the performances of the supervised learning-based methods are also very sensitive to the quality of the training set utilized. To address these problems, this paper presents a novel unsupervised method for inferring the home locations of the OSN users. The method first builds a heterogeneous network model to comprehensively represent the complex location information in the OSN data and then recursively infers users' home locations by fusing the direct and indirect location information of the users. Experiments that compared our method with five existing typical Twitter user home location inference methods on a Twitter dataset demonstrate that the proposed method can significantly improve the accuracy and reliability of user home location inference.