CompactMap: A mental map preserving visual interface for streaming text data

Abstract

As text streams become increasingly available from social media such as Facebook and Twitter, visual analysis of streaming text data is playing an important role in most business sectors. A fundamental challenge in visualizing a large amount of streaming text data is to preserve the user's mental map to enable tracking dynamic changes in topics, while simultaneously utilizing the display space efficiently. In this paper, we present CompactMap, an online visual interface that packs text clusters efficiently, with stable updates to maintain the user's mental map. It achieves spatiotemporally coherent layouts by dynamically matching clusters across time, and removing cluster overlaps according to spatial proximity and constraints. We developed a visual search engine based on CompactMaps for exploring a large amount of text streams in details on demand. We demonstrate the effectiveness of our approach in a controlled user study compared with a competing method.