Human Mobility Prediction Based on Social Media with Complex Event Processing

The combination of mobile and social media sensors is foreseen to become a crucial course of action so as to comprehensively capture and understand the movement of people in large spatial regions. In that sense, the present work describes a novel personal location predictor that makes use of these two types of sensors. Firstly, it extracts the mobility models of an area capturing aspects related to particular users along with crowd-based features on the basis of geotagged tweets. Unlike previous approaches, the proposed solution mines such models in an online manner so that no previous off-line training is required. Then, on the basis of such models, a predictor able to forecast the next activity and position of a user is developed. Finally, the described approach is tested by using Twitter datasets from two different cities.