Visual abstraction and exploration of large-scale geographical social media data

Abstract

A great deal of text and geographical information is provided in the geo-tagged social media data, which offers unprecedented opportunities to get insights into the social behaviors across different local areas. With the increasing size of geo-tagged social media data, a large number of visual mapping elements overlap with each other, which makes it difficult to visually capture topics of interest as well as their spatial distribution. In this paper, we propose a visual abstraction framework for the exploration of large scale geo-tagged social media data. Probabilistic topic modeling is firstly utilized to summarize the semantics of texts and extract a set of topic features of interest. Then, a multi-objective sampling model is designed to generate a subset of original dataset, which will not only reduce the visual clutter of large-scale social media data visualization, but also retain the ordering of topic features of interest as well as the geographical distribution of original social media datasets. A rich set of visual designs such as word cloud, text stream and heat map are integrated into the visual abstraction framework, enabling users to evaluate the sampled results from different perspectives including semantic topics, temporal changes and spatial distribution. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in simplifying the geographical visualization of large scale geo-tagged social media data and exploring the social behaviors across different local areas.