Conch: Competitive Debate Analysis via Visualizing Clash Points and Hierarchical Strategies
Qianhe Chen, Yong Wang, Yixin Yu, Xiyuan Zhu, Xuerou Yu, Ran Wang

TL;DR
Conch is an interactive visualization tool that uses novel spiral diagrams and large language models to analyze and understand the evolution of debates, helping participants improve their argumentative skills.
Contribution
We introduce Conch, a system combining visualizations and LLMs to analyze debate dynamics and strategies automatically and interactively.
Findings
Effective visualization of debate evolution
Automated identification of clash points and strategies
Positive user study results on usability
Abstract
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,…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Sentiment Analysis and Opinion Mining · Multi-Agent Systems and Negotiation
