面向沉浸式网络可视化与分析的基于语音交互的设计研究
A Design Study on Voice-based Interaction for Immersive Network Visualization and Analysis
AI总结：
本研究通过研究驱动设计，设计了依托大语言模型的沉浸式网络可视化语音交互系统，经用户研究表明其可提升感知可用性、降低认知负荷，为沉浸式可视化提供设计启示。
AI中文摘要：
视觉网络分析利用网络可视化创作技术，助力对网络数据的意义建构、偶然发现与假设验证。然而，将同一范式迁移至沉浸式环境并非易事，因创作操作的UI可用性不足。研究者已探索多模态交互的结合，但这类输入系统的高学习曲线限制了普通数据分析师的采用，更遑论网络分析。本研究通过研究驱动设计（RtD）开展以语音作为主要输入模态的优势与局限研究，设计了一套依托大语言模型（LLMs）实现沉浸式网络可视化语音交互的系统。针对社会网络数据分析开展用户研究，参与者涵盖社会科学与计算机科学背景，结果显示：与基于控制器的交互相比，语音交互可提升感知可用性，且能降低指令表述的认知负荷，因为用户可通过自然语言表达意图，而非压缩为简洁指令。本文还探讨了沉浸式可视化的设计启示，强调可用性限制系统采用，而简化交互与语音控制可提升流畅度，支持复杂多参数操作。
英文摘要：
Visual network analysis leverages network visualization authoring techniques to facilitate sensemaking, serendipitous discovery, and hypothesis verification on network data. However, transferring the same paradigm to immersive environments is non-trivial due to insufficient UI affordance for authoring operations. Researchers have studied combining multiple modalities for interactions, but the high learning curve of such input systems limits their adoption by typical data analysts, let alone for network analytics. In this work, we investigate the advantages and limitations of voice as the primary input modality with a research-through-design (RtD) study, in which we design a system that supports voice-based interactions for immersive network visualization facilitated by Large Language Models (LLMs). Through a user study on social network data analysis with participants from social science and computer science backgrounds, we find that voice interactions can improve perceived usability relative to controller-based interaction and lower the cognitive effort of formulating commands, since users can express intent in natural language rather than compressing it into terse instructions. We discuss design implications for immersive visualizations, highlighting how usability limits adoption while simplified interactions and voice-based controls enhance fluidity and support complex, multi-parameter operations.
