Geosemantic Network-of-Interest Construction Using Social Media Data Full text

Sophia Karagiorgou, Dieter Pfoser, Dimitrios Skoutas
Geographic Information Science
Abstract. Within the last years, an ever increasing amount of data from mobile and navigation devices (e.g. web check-in, vehicle tracking data, etc), as well as social media (e.g. Twitter) are becoming available, presenting and enabling new research challenges and applications. To unveil persistent and meaningful knowledge from user-generated location-based “stories”, this work proposes a novel methodology that converts Twitter check-in data into a mixed geo-semantic network-of-interest (NOI). It does so by introducing a novel network construction algorithm on segmented input data based on different mobility types. This produces network layers by means of behavioral and geometric trajectories, which are then combined into a single network. This segmentation addresses also the challenges imposed by noisy, low-sampling rate trajectories. An experimental evaluation assesses the quality of the algorithms by constructing the a network from trajectories based on Twitter check-in data for London and New York. Our results show that this method is robust and provides accurate and interesting results.