Knittel, JohannesKoch, SteffenErtl, ThomasMadeiras Pereira, João and Raidou, Renata Georgia2019-06-022019-06-022019978-3-03868-088-8https://doi.org/10.2312/eurp.20191134https://diglib.eg.org:443/handle/10.2312/eurp20191134This work presents a new approach to visually summarize large micro-document collections such as tweets. We extract frequent patterns of phrases as shortened quotes to present analysts an overview of popular snippets and statements, enabling more specific insights into large text collections compared to keyword-based visualizations. In our hierarchical structure, each quote can be the starting point to extract more fine-grained patterns on a subset of sentences that match the parent pattern. We show that our approach is scalable by applying it to millions of tweets.Humancentered computingVisual analyticsComputing methodologiesInformation extraction10.2312/eurp.2019113413-15