Location and Trajectory Identification from Microblogs

The rapid development of social networks has resulted in a proliferation of user-generated content (UGC), which can benefit many applications. In this paper, we study the problem of identifying a user’s locations from microblogs, to facilitate effective location-based advertisement and recommendation. Since the location information in a microblog is incomplete, we cannot get an accurate location from a local microblog. As such, we propose a global location identification method, G litter . G litter combines multiple microblogs of a user and utilizes them to identify the user’s locations. G litter not only improves the quality of identifying a user’s location but also supplements the location of a microblog so as to obtain an accurate location of a microblog. To facilitate location identification, G litter organizes points of interest (POIs) into a tree structure where leaf nodes are POIs and non-leaf nodes are segments of POIs, e.g., countries, cities, and streets. Using the tree structure, G litter first extracts candidate locations from each microblog of a user which correspond to some tree nodes. Then G litter aggregates these candidate locations and identifies top- k locations of the user. Using the identified top- k user locations, G litter refines the candidate locations and computes top- k locations of each microblog. To achieve high recall, we enable fuzzy matching between locations and microblogs. We propose an incremental algorithm to support dynamic updates of microblogs. We also study how to identify users’ trajectories based on the extracted locations. We propose an effective algorithm to extract high-quality trajectories. Experimental results on real-world datasets show that our method achieves high quality and good performance, and scales well.