LAUSR

With the rise of social networking and the awareness of privacy protection, the large-scale positioning data is getting harder to access while the clickstream data of social networking is accumulating. It is necessary to conduct a comprehensive study on the temporal property and spatial distribution of social network’s clickstream to determine the latent relationship between clickstream and location semantics which semantically annotate locations to help us understand the purpose of a social network user at a certain location and a particular time. In this paper, we first infer the location semantics from Internet logs as our baseline which is a large scale, imprecise positioning mobile dataset through combining the utilization of derived time pattern and speed pattern. Then we design a method to reconstruct location semantics through the social network. We are the first to our knowledge that define “click motif” of social network’s clickstream which is the combination of clicks in clickstream that occurs repeatedly and has significance. Click motif not only can help us to classify location semantics but also enhance the user experience of social networking. Besides, we propose a completed process to extract typical click motifs from a large-scale complicated clickstream data via embedding and clustering method. By extensive experiments and analysis, we demonstrate that different combination of click motifs can convey the users’ location semantics, therefore clickstream may disclose location privacy.