Kavli Affiliate: Ran Wang
| First 5 Authors: Qianhe Chen, Qianhe Chen, , ,
| Summary:
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.
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