Beomap: Ad Hoc Topic Maps for Enhanced Exploration of Social Media Data

Social media is ubiquitous. There is a need for intelligent retrieval interfaces that will enable a better understanding, exploration and browsing of social media data. A novel two dimensional ad hoc topic map is proposed called Beomap. The main novelty of Beomap is that it allows a user to define an ad hoc semantic dimension with a keyword query when visualizing topics in text data. This not only helps to impose more meaningful spatial dimensions for visualization, but also allows users to steer browsing and exploration of the topic map through ad hoc defined queries. We developed a system to implement Beomap for exploring Twitter data, and evaluated the proposed Beomap in two ways, including an offline simulation and a user study. Results of both evaluation strategies show that the new Beomap interface is better than a standard interactive interface.