Geosemantic Network-of-Interest Construction Using Social Media Data

An ever increasing amount of geospatial data generated by mobile devices and social media applications becomes available and presents us with applications and also research challenges. The scope of this work is to discover persistent and meaningful knowledge from user-generated location-based “stories” as reported by Twitter data. We propose a novel methodology that converts geocoded tweets into a mixed geosemantic network-of-interest (NOI). It does so by introducing a novel network construction algorithm on segmented input data based on discovered mobility types. The generated network layers are then combined into a single network. This segmentation addresses also the challenges imposed by noisy, low-sampling rate “social media” trajectories. An experimental evaluation assesses the quality of the algorithms by constructing networks for London and New York. The results show that this method is robust and provides accurate and interesting results that allow us to discover transportation hubs and critical transportation infrastructure.