From tweets to semantic trajectories: mining anomalous urban mobility patterns

This paper proposes and experiments new techniques to detect urban mobility patterns and anomalies by analyzing trajectories mined from publicly available geo-positioned social media traces left by the citizens (namely Twitter). By collecting a large set of geo-located tweets characterizing a specific urban area over time, we semantically enrich the available tweets with information about its author - i.e. a res- ident or a tourist - and the purpose of the movement - i.e. the activity performed in each place. We exploit mobility data mining techniques together with social net- work analysis methods to aggregate similar trajectories thus pointing out hot spots of activities and flows of people together with their varia- tions over time. We apply and validate the proposed trajectory mining approaches to a large set of trajectories built from the geo-positioned tweets gathered in Barcelona during the Mobile World Congress 2012 (MWC2012), one of the greatest events that affected the city in 2012.