Browsing by Author "Heer, Jeffrey"
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Item Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web(The Eurographics Association and John Wiley & Sons Ltd., 2019) Conlen, Matthew; Kale, Alex; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, HeikeJournalists, educators, and technical writers are increasingly publishing interactive content on the web. However, popular analytics tools provide only coarse information about how readers interact with individual pages, and laboratory studies often fail to capture the variability of a real-world audience. We contribute extensions to the Idyll markup language to automate the detailed instrumentation of interactive articles and corresponding visual analysis tools for inspecting reader behavior at both micro- and macro-levels. We present three case studies of interactive articles that were instrumented, posted online, and promoted via social media to reach broad audiences, and share data from over 50,000 reader sessions. We demonstrate the use of our tools to characterize article-specific interaction patterns, compare behavior across desktop and mobile devices, and reveal reading patterns common across articles. Our contributed findings, tools, and corpus of behavioral data can help advance and inform more comprehensive studies of narrative visualization.Item Latent Space Cartography: Visual Analysis of Vector Space Embeddings(The Eurographics Association and John Wiley & Sons Ltd., 2019) Liu, Yang; Jun, Eunice; Li, Qisheng; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, HeikeLatent spaces-reduced-dimensionality vector space embeddings of data, fit via machine learning-have been shown to capture interesting semantic properties and support data analysis and synthesis within a domain. Interpretation of latent spaces is challenging because prior knowledge, sometimes subtle and implicit, is essential to the process. We contribute methods for ''latent space cartography'', the process of mapping and comparing meaningful semantic dimensions within latent spaces. We first perform a literature survey of relevant machine learning, natural language processing, and scientific research to distill common tasks and propose a workflow process. Next, we present an integrated visual analysis system for supporting this workflow, enabling users to discover, define, and verify meaningful relationships among data points, encoded within latent space dimensions. Three case studies demonstrate how users of our system can compare latent space variants in image generation, challenge existing findings on cancer transcriptomes, and assess a word embedding benchmark.