In-depth analysis of competitive debates is essential for participants to develop argumentative skills and refine strategies, and further improve their debating performance. However, manual analysis of unstructured and unlabeled textual records of debating is time-consuming and ineffective, as it is challenging to reconstruct contextual semantics and track logical connections from raw data. To address this, we propose Conch, an interactive visualization system that systematically analyzes both what is debated and how it is debated. In particular, we propose a novel parallel spiral visualization that compactly traces the multidimensional evolution of clash points and participant interactions throughout debate process. In addition, we leverage large language models with well-designed prompts to automatically identify critical debate elements such as clash points, disagreements, viewpoints, and strategies, enabling participants to understand the debate context comprehensively. Finally, through two case studies on real-world debates and a carefully-designed user study, we demonstrate Conch's effectiveness and usability for competitive debate analysis.

翻译：对竞技辩论进行深入分析对于参与者提升论证技巧、优化策略并进一步提高辩论表现至关重要。然而，由于原始数据中难以重建上下文语义并追踪逻辑关联，对非结构化且无标注的辩论文本记录进行人工分析既耗时又低效。为此，我们提出了Conch——一个交互式可视化系统，能够系统性地分析辩论内容（what）与辩论方式（how）。具体而言，我们设计了一种新颖的平行螺旋可视化方法，紧凑地追踪辩论过程中冲突点与参与者互动的多维演化轨迹。此外，我们利用大语言模型结合精心设计的提示词，自动识别关键辩论要素（如冲突点、分歧、观点与策略），帮助参与者全面理解辩论语境。最后，通过两个真实辩论案例研究及一项精心设计的用户实验，我们验证了Conch在竞技辩论分析中的有效性与可用性。