%0 Journal Article %J Big Data Research %D 2019 %T Interactive Visual Analytics for Sensemaking with Big Text %A Michelle Dowling %A Nathan Wycoff %A Brian Mayer %A Wenskovitch, John %A Leman, Scotland %A House, Leanna %A Nicholas Polys %A North, Chris %A Peter Hauck %K Big data %K interactive visual analytics %K Semantic interaction %K text analytics %K Topic modeling %K visualization %X Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles. %B Big Data Research %V 16 %P 49 - 58 %8 July/2019 %U http://www.sciencedirect.com/science/article/pii/S2214579618302995 %R https://doi.org/10.1016/j.bdr.2019.04.003 %0 Conference Proceedings %B 2019 Symposium on Visualization in Data Science Posters %D 2019 %T Uncertainty in Interactive WMDS Visualizations %A Lata Kodali %A Wenskovitch, John %A Nathan Wycoff %A House, Leanna %A North, Chris %K poster %X Visualizations are useful when learning from high-dimensional data. However, visualizations can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. In our work, we extend a framework called Bayesian Visual Analytics (BaVA) on a dimension reduction algorithm, Weighted Multidimensional Scaling (WMDS), to incorporate uncertainty as analysts explore data visually. BaVA-WMDS visualizations are interactive, and possible interactions include manipulating variable weights and/or the coordinates of the two-dimensional projection. Uncertainty exists in these visualizations on the variable weights, the user interactions, and the fit of WMDS. We quantify these uncertainties using Bayesian models exploring randomness in both coordinates and weights in a method we call Interactive Probabilistic WMDS (IP-WMDS). Specifically, we use posterior estimates to assess fit of WMDS, the range of motion of coordinates, as well as variability in weights. Visually, we display such uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in fitting a dimension reduction algorithm. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts. %B 2019 Symposium on Visualization in Data Science Posters %S VDS'19 %C Vancouver, BC, Canada %0 Journal Article %J IEEE Transactions on Learning Technologies %D 2018 %T Be the Data: Embodied Visual Analytics %A Xin Chen %A Self, Jessica Zeitz %A House, Leanna %A Wenskovitch, John %A Sun, Maoyuan %A Nathan Wycoff %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %B IEEE Transactions on Learning Technologies %V 11 %P 81-95 %N 1 %R 10.1109/TLT.2017.2757481 %0 Conference Paper %B IEEE International Symposium on Big Data Visual Analytics %D 2015 %T Big Text Visual Analytics in Sensemaking %A Lauren Bradel %A Nathan Wycoff %A House, Leanna %A North, Chris %B IEEE International Symposium on Big Data Visual Analytics %P 8 pages %8 09/2015