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User-generated content is a valuable source of information whose production increases year after year. Twitter data is a form of user-generated content that is frequently adopted to comment on life activities in several contexts. Thus, scientific interest in that data has increased in recent years. This paper focusses on visual analytics approaches addressing the microblogging content exchanged through Twitter. In particular, we concentrate our interest on approaches that consider spatial and temporal aspects and provide visual support. Articles from the major conferences, journals, and digital libraries have been collected, organized and compared based on different criteria such as research questions, application focus, analytical and visual methods adopted, and interaction provided. In addition to these comparisons, opportunities and challenges are illustrated to inspire future research.

Abstract. Understanding novel visualizations can be a challenge even for experienced users. During iterative usability engineering phases in the DisCō project, visualizations of time-oriented data are explored by domain experts and non-experts. The aim of our study is to analyze the generation of knowledge and understanding by means of visualizations without previous user training. Focusing on applicability in various business domains for personnel planning and time scheduling, we tested mockups of visualizations with a method based on user-reported insights. Results show almost identical behavior of domain experts and non-experts when generating insights into the data from scratch. In the course of working with a visualization, an interchange of insights into the visualization and insights into the data was found.

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