LLMs support data analysis through conversational user interfaces, as exemplified in OpenAI’s ChatGPT (formally known as Advanced Data Analysis or Code Interpreter). Essentially, LLMs produce code for accomplishing diverse analysis tasks. However, presenting raw code can obscure the logic and hinder user verification. To empower users with enhanced comprehension and augmented control over analysis conducted by LLMs, we propose a novel approach to transform LLM-generated code into an interactive visual representation. In the approach, users are provided with a clear, step-by-step visualization of the LLM-generated code in real time, allowing them to understand, verify, and modify individual data operations in the analysis. Our design decisions are informed by a formative study (N=8) probing into user practice and challenges. We further developed a prototype named WaitGPT and conducted a user study (N=12) to evaluate its usability and effectiveness. The findings from the user study reveal that WaitGPT facilitates monitoring and steering of data analysis performed by LLMs, enabling participants to enhance error detection and increase their overall confidence in the results.

Communicative visualizations play a crucial role in effectively conveying information about the data to diverse audiences. Authoring these visualizations often requires a blend of technical skill and creativity to design engaging and memorable representations. This talk explores the intersection of creativity support and the authoring process for communicative visualizations. We will review representative works in this area, highlighting their key contributions to enabling and enhancing creative processes in visualization design. Through these examples, we will identify common themes and challenges when developing tools and techniques to support creativity in this domain. The talk will conclude by discussing potential avenues for future research and development, especially with the recent advance of generative AI.

With appealing visual effects, kinetic typography (animated text) has prevailed in movies, advertisements, and social media. However, it remains challenging and time-consuming to craft its animation scheme. We propose an automatic framework to transfer the animation scheme of a rigid body on a given meme GIF to text in vector format. First, the trajectories of key points on the GIF anchor are extracted and mapped to the text’s control points based on local affine transformation. Then the temporal positions of the control points are optimized to maintain the text topology. We also develop an authoring tool that allows intuitive human control in the generation process. A questionnaire study provides evidence that the output results are aesthetically pleasing and well preserve the animation patterns in the original GIF, where participants were impressed by a similar emotional semantics of the original GIF. In addition, we evaluate the utility and effectiveness of our approach through a workshop with general users and designers.

在现代沟通的语境下，AI在叙事型可视化应用，已经成为推动信息和情感传达创新的关键力量。该报告立足于文字这一可视化中的基础图形元素，结合两个相关工作，探讨AI如何丰富动态文字的情感表达。第一个工作通过运动迁移技术辅助生成动态文字。用户只需提供一个表情包动图，AI即可捕捉其动态特征并将其应用到文本上，创造出与原图情感语义一致的动态文字。这一过程不仅突破了传统设计的界限，还为非专业用户提供了创作生动文字动画的可能性。而第二个工作则将动态文字扩展到词云，为其中每个词赋予特定情感的动效，并通过全局参数的调整，让用户能够精确控制情感表达的细节。这不仅提高了情感可视化的准确性，也增强了故事叙述的情感沟通力。在本次讨论中，我们将深入分析这些AI驱动的工具和技术是如何使得个人和专业设计师在创作过程中的工作更为高效，以及它们如何帮助用户更加直观地表达和分享他们的情感。同时，我们也将展示这些技术如何帮助我们更好地理解和利用可视化工具在数字媒体叙事中的作用。

We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to the audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the delivered emotion extent. We also invited general users to create emordles on their own based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.

We present MeasureFlow, an interface to visually and interactively explore dynamic networks through time-series of network measures such as link number, graph density, or node activation. When networks contain many time steps, become large and more dense, or contain high frequencies of change, traditional visualizations that focus on network topology, such as animations or small multiples, fail to provide adequate overviews and thus fail to guide the analyst towards interesting time points and periods. MeasureFlow presents a complementary approach that relies on visualizing time-series of common network measures to provide a detailed yet comprehensive overview of when changes are happening and which network measures they involve. As dynamic networks undergo changes of varying rates and characteristics, network measures provide important hints on the pace and nature of their evolution and can guide an analysts in their exploration; based on a set of interactive and signal-processing methods, MeasureFlow allows an analyst to select and navigate periods of interest in the network. We demonstrate MeasureFlow through case studies with real-world data.