IEEE Pacific Visualization Symposium : [proceedings]

Scatterplots are widely used in Explainable Artificial Intelligence (XAI) to investigate misclassifications and patterns across instances. However, a significant limitation of scatterplots is overplotting, especially when working with large datasets. Although several quality metrics have been proposed to measure the degree of overplotting, none have been demonstrated to be effective in the context of XAI. This paper aims to evaluate the effectiveness of a quality metric, called OM4AnI, in XAI scenarios. We begin by summarizing two visual patterns—cluster-based and regression-based patterns—that support three common XAI tasks: feature importance, feature dependency, and model accuracy. We also introduce how to select the parameters of OM4AnI based on these patterns. We construct two case studies to identify the effectiveness of OM4AnI using public datasets: Census Income dataset and MNIST dataset. OM4AnI is applied to both scenarios under various visual conditions (e.g., marker size and rendering order) to assess its effectiveness. The results demonstrate that OM4AnI serves as an effective quality metric for these two common XAI scenarios, paving the way for adapting other quality metrics to be scalable within XAI contexts.

Large Language Models (LLMs) are becoming a key part of interactive visual analytics systems, helping users engage in natural language interaction and explore data more effectively. However, this kind of support can sometimes unintentionally reinforce confirmation bias by consistently aligning responses with what the user has already expected and focused on. In this work, we introduce an event-driven workflow to raise awareness of confirmation bias, leveraging naturally occurring interaction traces (e.g., navigation patterns, content choices, time spent on items, and query formulation) to detect signs of confirmation bias during exploration. We apply our proposed method in the realm of Consumer Health Information Systems (CHIS), where mitigating biased information seeking and processing is particularly critical. Our work lays the groundwork for future research on cognitive bias-aware LLM agents, extending beyond confirmation bias to other forms of cognitive biases in visual analytics systems.

Multimodal packet streams—video synchronized with telemetry, pose estimates, and runtime events—increasingly support real-time monitoring in robotics, digital twins, and sensor networks. Frequency-domain diagnostics are attractive in this setting because they can localize where a rendered view deviates from the observation, yet they are often validated under simplified previous-frame proxies whose conclusions may not transfer to real rendering pipelines. We present Coherent Frequency Packet Splatting for Dynamics (CFPS-DYN), a visual analytics methodology that computes an Explainable Frequency Heatmap via a tiled, phase-sensitive spectral residual between the incoming observation and the current prediction. We evaluate five diagnostic representations—pixel residual, FFT amplitude, phase coherence, and two packet-based variants—under both a previous-frame proxy and real 3D Gaussian Splatting (3DGS) renders on TUM RGB-D sequences. Under the proxy, the pixel baseline exhibits an inflated correlation with temporal instability (r=0.920), but this oracle advantage collapses under real 3DGS renders (r=0.041) when temporal information is no longer leaked into the residual. We empirically observe a decoupling between temporal instability and reconstruction fidelity under real 3DGS renders, with stable-but-incorrect outputs occurring in high-error regions. In this regime, frequency-domain packet scalar energy is the strongest predictor of reconstruction error (r=0.236, p<10−7), outperforming pixel residuals. A linked-view interface exposes heatmap overlays and timeline prioritization to support monitoring and intervention.

The use of GPS in sports through personal fitness trackers has become widespread. For amateur team sports, the decreasing cost of GPS devices is making tracking devices a more viable option. However, once the data is generated, there are still barriers in accessing tools to explore the group trajectories in ways that would allow amateur coaches or players to gain positional information with strategic relevance. This paper presents an initial investigation into the requirements of amateur football (soccer). Six coaches and players were interviewed from three different amateur football clubs in Australia. Thematic analysis was used to identify key requirements for group GPS trajectory visualization. A visual analytics prototype was designed, developed, and evaluated with the participants. Amateur football visual analysis barriers, improvements for the prototype, and future work are discussed.

Sentiment analysis plays a critical role in understanding public opinion, particularly in political discourse, where sentiments evolve in response to historical events and collective emotional dynamics. However, existing sentiment analysis research largely overlooks time-grounded sociopolitical contexts and domain-specific historical data, especially for low-resource languages such as Bengali. Moreover, there are currently very few datasets that systematically integrate temporal political events with fine-grained sentiment intensity annotations. To address this gap, we introduce a time-grounded, context-aware Bengali political sentiment dataset aligned with major sociopolitical events in Bangladesh from July 2024 to August 2025. The dataset consists of manually curated political texts, annotated with a five-level sentiment intensity scale ranging from strongly negative to strongly positive. Annotation reliability is ensured through multi-annotator agreement, contextual validation using reaction signals, and GPT-assisted verification. Building on this dataset, we perform fine-grained linguistic and sentiment-intensity analyses to examine how political sentiment evolves over time and across sociopolitical contexts. We fine-tuned the existing BanglaBERT model, which is lightweight and enhanced with an attention-refinement mechanism that enables the effective capture of time-sensitive contextual signals in a low-resource setting. The proposed mechanism achieves 93.52% accuracy and a macro-F1 score of 92.70% while maintaining low computational complexity. Finally, we integrate the model into a confidence-aware visual analytics framework that supports exploratory analysis of temporally evolving political sentiment, demonstrating the practical value of the dataset and approach.