Socrates: Data Story Generation via Adaptive Machine-Guided Elicitation of User Feedback.

Visual data stories can effectively convey insights from data, yet their creation often necessitates intricate data exploration, insight discovery, narrative organization, and customization to meet the communication objectives of the storyteller. Existing automated data storytelling techniques, however, tend to overlook the importance of user customization during the data story authoring process, limiting the system's ability to create tailored narratives that reflect the user's intentions. We present a novel data story generation workflow that leverages adaptive machine-guided elicitation of user feedback to customize the story. Our approach employs an adaptive plug-in module for existing story generation systems, which incorporates user feedback through interactive questioning based on the conversation history and dataset. This adaptability refines the system's understanding of the user's intentions, ensuring the final narrative aligns with their goals. We demonstrate the feasibility of our approach through the implementation of an interactive prototype: Socrates. Through a quantitative user study with 18 participants that compares our method to a state-of-the-art data story generation algorithm, we show that Socrates produces more relevant stories with a larger overlap of insights compared to human-generated stories. We also demonstrate the usability of Socrates via interviews with three data analysts and highlight areas of future work.

Evaluating the Use of Uncertainty Visualisations for Imputations of Data Missing At Random in Scatterplots.

Most real-world datasets contain missing values yet most exploratory data analysis (EDA) systems only support visualising data points with complete cases. This omission may potentially lead the user to biased analyses and insights. Imputation techniques can help estimate the value of a missing data point, but introduces additional uncertainty. In this work, we investigate the effects of visualising imputed values in charts using different ways of representing data imputations and imputation uncertainty-no imputation, mean, 95% confidence intervals, probability density plots, gradient intervals, and hypothetical outcome plots. We focus on scatterplots, which is a commonly used chart type, and conduct a crowdsourced study with 202 participants. We measure users' bias and precision in performing two tasks-estimating average and detecting trend-and their self-reported confidence in performing these tasks. Our results suggest that, when estimating averages, uncertainty representations may reduce bias but at the cost of decreasing precision. When estimating trend, only hypothetical outcome plots may lead to a small probability of reducing bias while increasing precision. Participants in every uncertainty representation were less certain about their response when compared to the baseline. The findings point towards potential trade-offs in using uncertainty encodings for datasets with a large number of missing values. This paper and the associated analysis materials are available at: https://osf.io/q4y5r/.

An Evaluation-Focused Framework for Visualization Recommendation Algorithms.

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.

Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways.

Well-designed data visualizations can lead to more powerful and intuitive processing by a viewer. To help a viewer intuitively compare values to quickly generate key takeaways, visualization designers can manipulate how data values are arranged in a chart to afford particular comparisons. Using simple bar charts as a case study, we empirically tested the comparison affordances of four common arrangements: vertically juxtaposed, horizontally juxtaposed, overlaid, and stacked. We asked participants to type out what patterns they perceived in a chart and we coded their takeaways into types of comparisons. In a second study, we asked data visualization design experts to predict which arrangement they would use to afford each type of comparison and found both alignments and mismatches with our findings. These results provide concrete guidelines for how both human designers and automatic chart recommendation systems can make visualizations that help viewers extract the "right" takeaway.

DeepCompare: Visual and Interactive Comparison of Deep Learning Model Performance.

Deep learning models have become the state-of-the-art for many tasks, from text sentiment analysis to facial image recognition. However, understanding why certain models perform better than others or how one model learns differently than another is often difficult yet critical for increasing their effectiveness, improving prediction accuracy, and enabling fairness. Traditional methods for comparing models' efficacy, such as accuracy, precision, and recall provide a quantitative view of performance; however, the qualitative intricacies of why one model performs better than another are hidden. In this paper, we interview machine learning practitioners to understand their evaluation and comparison workflow. From there, we iteratively design a visual analytic approach, DeepCompare, to systematically compare the results of deep learning models, in order to provide insight into the model behavior and interactively assess tradeoffs between two such models. The tool allows users to evaluate model results, identify and compare activation patterns for misclassifications, and link the test results back to specific neurons. We conduct a preliminary evaluation through two real-world case studies to show that experts can make more informed decisions about the effectiveness of different types of models, understand in more detail the strengths and weaknesses of the models, and holistically evaluate the behavior of the models.