Storytelling infographics are a powerful medium for communicating data-driven stories through visual presentation. However, existing authoring tools lack support for maintaining story consistency and aligning with users’ story goals throughout the design process. To address this gap, we conducted formative interviews and a quantitative analysis to identify design needs and common story-informed layout patterns in infographics. Based on these insights, we propose a narrative-centric workflow for infographic creation consisting of three phases: story construction, visual encoding, and spatial composition. Building on this workflow, we developed InfoAlign, a human–AI co-creation system that transforms long or unstructured text into stories, recommends semantically aligned visual designs, and generates layout blueprints. Users can intervene and refine the design at any stage, ensuring their intent is preserved and the infographic creation process remains transparent. Evaluations show that InfoAlign preserves story coherence across authoring stages and effectively supports human–AI co-creation for storytelling infographic design.

The sudden influx of “TikTok refugees” into the Chinese platform RedNote in early 2025 created an unprecedented, large-scale online cross-cultural communication event between the West and East. Although prior HCI research has studied user behavior in social media, most work remains confined to monolingual or single-cultural contexts, leaving cross-linguistic and cultural dynamics underexplored. To address this gap, we focused on a particularly challenging cross-cultural encoding–decoding task that remains stubbornly beyond the reach of machine translation, i.e., foreign newcomers asking Chinese users for Chinese names, and examined how people collectively constructed a digital “Babel Tower” through various information encoding strategies. We collected and analyzed over 70,000 comments from RedNote with a creative human-in-the-loop approach using large language models, deriving a systematic framework summarizing cross-cultural information encoding strategies, how they are combined and layered to complicate decoding, and how they relate to engagement metrics such as the number of likes.

Virtual Reality (VR) broadcasting has emerged as a promising medium for providing immersive viewing experiences of major sports events such as tennis. However, current VR broadcast systems often lack an effective camera language and do not adequately incorporate dynamic, in-game visualizations, limiting viewer engagement and narrative clarity. To address these limitations, we analyze 400 out-of-play segments from eight major tennis broadcasts to develop a tennis-specific design framework that effectively combines cinematic camera movements with embedded visualizations. We further refine our framework by examining 25 cinematic VR animations, comparing their camera techniques with traditional tennis broadcasts to identify key differences and inform adaptations for VR. Based on data extracted from the broadcast videos, we reconstruct a simulated game that captures the players' and ball's motion and trajectories. Leveraging this design framework and processing pipeline, we develope Beyond the Broadcast, a VR tennis viewing system that integrates embedded visualizations with adaptive camera motions to construct a comprehensive and engaging narrative. Our system dynamically overlays tactical information and key match events onto the simulated environment, enhancing viewer comprehension and narrative engagement while ensuring perceptual immersion and viewing comfort. A user study involving tennis viewers demonstrate that our approach outperforms traditional VR broadcasting methods in delivering an immersive, informative viewing experience.

Figure 1: An overview of KinemaFX.KinemaFX supports interactive particle effect exploration and customization through three stages: (a) User Intent Input.Users can express their initial exploration intent by combining semantic input (a1) and graphical input (a2).(b) Effect Exploration.Kinematic supports particle effects searching based on controllable weights of semantic and kinematic similarity (b1).Users iteratively explore the space by selecting satisfying effects, thereby implicitly conveying their preferences(b2).(c) Effect Composition.From the explored particle effects, users can select (c1) and control individual effects' transformations and temporal features (c2) to compose effect artworks (c3).

The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges, we developed DTBIA, an Immersive Visual Analytics System for Brain-Inspired Research. In collaboration with domain experts, we identified key requirements for effectively visualizing spatiotemporal and topological patterns at multiple levels of detail. DTBIA incorporates a hierarchical workflow - ranging from brain regions to voxels and slice sections - along with immersive navigation and a 3D edge bundling algorithm to enhance clarity and provide deeper insights into both functional (BOLD) and structural (DTI) brain data. The utility and effectiveness of DTBIA are validated through two case studies involving with brain research experts. The results underscore the system's role in enhancing the comprehension of complex neural behaviors and interactions.

Recent advancements in Graph Neural Networks (GNNs) show promise for various applications like social networks and financial networks. However, they exhibit fairness issues, particularly in human-related decision contexts, risking unfair treatment of groups historically subject to discrimination. While several visual analytics studies have explored fairness in machine learning (ML), few have tackled the particular challenges posed by GNNs. We propose a visual analytics framework for GNN fairness analysis, offering insights into how attribute and structural biases may introduce model bias. Our framework is model-agnostic and tailored for real-world scenarios with multiple and multinary sensitive attributes, utilizing an extended suite of fairness metrics. To operationalize the framework, we develop GNNFairViz, a visual analysis tool that integrates seamlessly into the GNN development workflow, offering interactive visualizations. Our tool enables GNN model developers, the target users, to analyze model bias comprehensively, facilitating node selection, fairness inspection, and diagnostics. We evaluate our approach through two usage scenarios and expert interviews, confirming its effectiveness and usability in GNN fairness analysis. Furthermore, we summarize two general insights into GNN fairness based on our observations on the usage of GNNFairViz, highlighting the prevalence of the "Overwhelming Effect" in highly unbalanced datasets and the importance of suitable GNN architecture selection for bias mitigation.

How to explore the fine-grained but meaningful information from the massive amount of social media data is critical but challenging. To address the challenge, we propose the TopicBubbler, a visual analytics system that supports the cross-level fine-grained exploration of social media data. To achieve the goal of cross-level fine-grained exploration, we proposed a new workflow. Under the procedure of the workflow, we construct the Fine-grained Exploration View through the design of bubble-based word clouds. Each bubble contains two rings that can display information through different levels, and recommends six keywords computed by different algorithms. The view supports users to collect information at different levels and to perform fine-grained selection and exploration across different levels based on keyword recommendations. To enable the users to explore the temporal information and the hierarchical structure, we also construct the Temporal View and Hierarchical View, which satisfy users to view the cross-level dynamic trends and the overview hierarchical structure. In addition, we use the storyline metaphor to enable users to consolidate the fragmented information extracted across levels and topics and ultimately present it as a complete story. Case studies from real-world data confirm the capability of the TopicBubbler from different perspectives, including event mining across levels and topics, and fine-grained mining of specific topics to capture events hidden beneath the surface.