Enabling Semantic User Context to Enhance Twitter Location Prediction

Prediction of user interest and behavior is currently an important research area in social network analysis. Most of the current prediction frameworks rely on analyzing user’s published contents and user’s relationships. Recently the dynamic nature of user’s modelling has been introduced in the prediction frameworks. This dynamic nature would be represented by time tagged attributes such as posts or location check-ins. In this paper, we study the relationships between geo-location information published by users at different times. This geo-location information was used to model user’s interest and behavior in order to enhance prediction of user locations. Furthermore, semantic features such as topics of interest and location category were extracted from this information in order to overcome sparsity of data. Several experiments on real twitter dataset showed that the proposed context-based prediction model which applies machine learning techniques outperformed traditional probabilistic location prediction model that only rely on words extracted from tweets associated with specific locations.