Discovering common semantic trajectories from geo-tagged social media

Abstract

Massive social media data are being created and uploaded to online nowadays. These media data associated with geographical information reflect people’s footprints of movements. This study investigates into extraction of people’s common semantic trajectories from geo-referenced social media data using geo-tagged images. We first convert geo-tagged photographs into semantic trajectories based on regions of-interest, and then apply density-based clustering with a similarity measure designed for multi-dimensional semantic trajectories. Using real geo-tagged photographs, we find interesting people’s common semantic mobilities. These semantic behaviors demonstrate the effectiveness of our approach.