RendezView: Look at Meanings of an Encounter Region over Local Social Flocks

Abstract

Social media data provide insight into people's opinions, thoughts, and reactions about real-world events such as hurricanes, infectious diseases, or urban crimes. In particular, the role of location-embedded social media is being emphasized to monitor surrounding situations and predict future effects by the geography of data shadows. However, it brings big challenges to find meaningful information about dynamic social phenomena from the mountains of fragmented, noisy data flooding. This paper proposes a data model to represent local flock phenomena as collective interests in geosocial streams and presents an interactive visual analysis process. In particular, we show a new visualization tool, called RendezView, composed of a three-dimensional map, word cloud, and Sankey flow diagram. RendezView allows a user to discern spatio-temporal and semantic contexts of local social flock phenomena and their co-occurrence relationships. An explanatory visual analysis of the proposed model is simulated by the experiments on a set of daily Twitter streams and shows the local patterns of social flocks with several visual results.