用大模型动态构建社交媒体分类体系，能跟上话题变化。
EvoTaxo: Building and Evolving Taxonomy from Social Media Streams
- 将每条帖子转为分类结构的修改建议，结合语义与时间局部性聚类
- 在两个Reddit数据集上生成更平衡、覆盖更全的分类结构
- 适合关注话题演化、需要自动更新分类体系的研究者
从社交媒体语料构建分类体系面临挑战：内容短小、噪声多、语义纠缠且随时间动态变化。现有方法多针对静态语料，难兼顾鲁棒性、可扩展性与对话语演化的敏感性。我们提出EvoTaxo，一种基于大语言模型的框架，用于从时序社交媒体流中构建与演化分类体系。不直接聚类原始帖子，而是将每条帖子转化为对当前分类体系的结构化修改建议，通过时间窗口累积结构证据，并采用双视角聚类融合语义相似性与时间局部性来整合候选修改。随后通过精炼与仲裁流程筛选可靠修改并执行，每个节点维护概念记忆库以长期保持语义边界。在两个Reddit数据集上的实验表明，EvoTaxo生成的分类体系比基线更平衡，帖子到叶节点的分配更清晰，相同规模下覆盖度更高，结构质量更强。对/r/ICE_Raids社区的案例研究显示，EvoTaxo能捕捉话语的重要时间演变。代码已公开。
原文摘要 · Abstract (English)
Constructing taxonomies from social media corpora is challenging because posts are short, noisy, semantically entangled, and temporally dynamic. Existing taxonomy induction methods are largely designed for static corpora and often struggle to balance robustness, scalability, and sensitivity to evolving discourse. We propose EvoTaxo, a LLM-based framework for building and evolving taxonomies from temporally ordered social media streams. Rather than clustering raw posts directly, EvoTaxo converts each post into a structured draft action over the current taxonomy, accumulates structural evidence over time windows, and consolidates candidate edits through dual-view clustering that combines semantic similarity with temporal locality. A refinement-and-arbitration procedure then selects reliable edits before execution, while each node maintains a concept memory bank to preserve semantic boundaries over time. Experiments on two Reddit corpora show that EvoTaxo produces more balanced taxonomies than baselines, with clearer post-to-leaf assignment, better corpus coverage at comparable taxonomy size, and stronger structural quality. A case study on the Reddit community /r/ICE_Raids further shows that EvoTaxo captures meaningful temporal shifts in discourse. Our codebase is available here.
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