Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ

Scattertext is an open source tool for visualizing linguistic variation between document categories in a language-independent way. The tool presents a scatterplot, where each axis corresponds to the rank-frequency a term occurs in a category of documents. Through a tie-breaking strategy, the tool is able to display thousands of visible term-representing points and find space to legibly label hundreds of them. Scattertext also lends itself to a query-based visualization of how the use of terms with similar embeddings differs between document categories, as well as a visualization for comparing the importance scores of bag-of-words features to univariate metrics.Comment: ACL 2017 Demos. 6 pages, 5 figures. See the Githup repo https://github.com/JasonKessler/scattertext for source code and documentatio

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

Exploring narrativity in data visualization in journalism

Many news stories are based on data visualization, and storytelling with data has become a buzzword in journalism. But what exactly does storytelling with data mean? When does a data visualization tell a story? And what are narrative constituents in data visualization? This chapter first defines the key terms in this context: story, narrative, narrativity, showing and telling. Then, it sheds light on the various forms of narrativity in data visualization and, based on a corpus analysis of 73 data visualizations, describes the basic visual elements that constitute narrativity: the instance of a narrator, sequentiality, temporal dimension, and tellability. The paper concludes that understanding how data are transformed into visual stories is key to understanding how facts are shaped and communicated in society

Analyzing Eye-Tracking Information in Visualization and Data Space: from Where on the Screen to What on the Screen.

Eye-tracking data is currently analyzed in the image space that gaze-coordinates were recorded in, generally with the help of overlays such as heatmaps or scanpaths, or with the help of manually defined areas of interest (AOI). Such analyses, which focus predominantly on where on the screen users are looking, require significant manual input and are not feasible for studies involving many subjects, long sessions, and heavily interactive visual stimuli. Alternatively, we show that it is feasible to collect and analyze eye-tracking information in data space. Specifically, the visual layout of visualizations with open source code that can be instrumented is known at rendering time, and thus can be used to relate gaze-coordinates to visualization and data objects that users view, in real time. We demonstrate the effectiveness of this approach by showing that data collected using this methodology from nine users working with an interactive visualization, was well aligned with the tasks that those users were asked to solve, and similar to annotation data produced by five human coders. Moreover, we introduce an algorithm that, given our instrumented visualization, could translate gaze-coordinates into viewed objects with greater accuracy than simply binning gazes into dynamically defined AOIs. Finally, we discuss the challenges, opportunities, and benefits of analyzing eye-tracking in visualization and data space

Exploring the Referral and Usage of Science Fiction in HCI Literature

Research on science fiction (sci-fi) in scientific publications has indicated the usage of sci-fi stories, movies or shows to inspire novel Human-Computer Interaction (HCI) research. Yet no studies have analysed sci-fi in a top-ranked computer science conference at present. For that reason, we examine the CHI main track for the presence and nature of sci-fi referrals in relationship to HCI research. We search for six sci-fi terms in a dataset of 5812 CHI main proceedings and code the context of 175 sci-fi referrals in 83 papers indexed in the CHI main track. In our results, we categorize these papers into five contemporary HCI research themes wherein sci-fi and HCI interconnect: 1) Theoretical Design Research; 2) New Interactions; 3) Human-Body Modification or Extension; 4) Human-Robot Interaction and Artificial Intelligence; and 5) Visions of Computing and HCI. In conclusion, we discuss results and implications located in the promising arena of sci-fi and HCI research.Comment: v1: 20 pages, 4 figures, 3 tables, HCI International 2018 accepted submission v2: 20 pages, 4 figures, 3 tables, added link/doi for Springer proceedin