SemMobi: A Semantic Annotation System for Mobility Data

Fei Wu, Hongjian Wang, Zhenhui Li, Wang-Chien Lee, Zhuojie Huang

Abstract: The wide adaptation of mobile devices embedded with modern positioning technology enables the collection of valuable mobility data from users. At the same time, the large-scale user-generated data from social media, such as geo-tagged tweets, provide rich semantic information about events and locations. The combination of the mobility data and social media data brings opportunities for us to study the semantics behind people's movement, i.e., understand why a person travels to a location at a particular time. Previous work have used map or POI (point of interest) database as source for semantics. However, those semantics are static, and thus missing important dynamic event information. To provide dynamic semantic annotation, we propose to use contextual social media. More specifically, the semantics could be landmark information (e.g., a museum or an arena) or event information (e.g., sports games or concerts). The SemMobi system implements our recently developed annotation method, which has been recently accepted to WWW 2015 conference. The annotation method annotates words to each mobility records based on local density of words, estimated by Kernel Density Estimation model. The annotated mobility data contain rich and interpretable information, therefore can benefit applications, such as personalized recommendation, targeted advertisement, and movement prediction. Our system is built upon large-scale tweet datasets. A user-friendly interface is designed to support interactive exploration of the result.