Visual analysis of microblog content using time-varying co-occurrence highlighting in tag clouds

The vast amount of contents posted to microblogging services each day offers a rich source of information for analytical tasks. The aggregated posts provide a broad sense of the informal conversations complementing other media. However, analyzing the textual content is challenging due to its large volume, heterogeneity, and time-dependence. In this paper, we exploit the idea of tag clouds to visually analyze microblog content. As a major contribution, tag clouds are extended by an interactive visualization technique that we refer to as time-varying co-occurrence highlighting. It combines colored histograms with visual highlighting of co-occurrences, thus allowing for a time-dependent analysis of term relations. An example dataset of Twitter posts illustrates the applicability and usefulness of the approach.