三层命名测量框架及其在代币发行平台上的普查应用
A Three-Layer Framework for Measuring Names and Its Census Application on a Token Launchpad
- School of Big Data, Baoshan University（宝山学院大数据学院）
- School of Business, Guangzhou College of Technology and Business（广州工商学院商学院）
- School of Computing Sciences, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology (PAF-IAST)（巴基斯坦-奥地利应用技术大学应用科学与技术研究所计算科学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一个三层框架测量资产名称，涵盖形式、参考和关系维度，并基于BNB链代币发行平台的513,647次命名尝试进行普查分析，揭示名称多样性、重复使用及文化事件响应等规律。
AI中文摘要：
资产名称影响市场行为，然而标准化的名称测量方法仍然缺乏。现有的处理流畅度测量主要针对字母语言，不适用于中文名称。文化含义通常需要人工编码，限制了大规模分析，而通过重复使用和语义拥挤进行的名称竞争仍未得到充分探索。本研究构建了一个包含513,647次命名尝试的数据集，这些尝试来自BNB链上this http URL代币发行平台，时间跨度为2026年2月至6月，并提出了一种用于名称测量的三层框架。形式层使用38个面向中文的特征来测量语言流畅度。参考层通过人工编码和大语言模型扩展来捕捉文化含义，并进行可靠性评估。关系层测量名称重复使用、语义拥挤和词汇变化。这三层在很大程度上是独立的，相关系数低于0.11。普查分析显示，名称多样性遵循希普斯定律，新名称的采用随时间下降，名称重复使用呈现重尾模式，并且重复命名发生在不同的创建者层面和跨创建者时间尺度上。文化事件也引发了快速的命名反应。该框架、带注释的数据集和代码已发布，以支持数字市场及其他命名环境中命名行为的可扩展分析。
英文摘要：
Asset names influence market behavior, yet standardized name measurement remains lacking. Existing processing fluency measures focus mainly on alphabetic languages and are unsuitable for Chinese names. Cultural meanings usually require manual coding, limiting large-scale analysis, while name competition through reuse and semantic crowding remains underexplored. This study constructs a dataset of 513,647 naming attempts from the Four token launchpad on BNB Chain between February and June 2026 and proposes a three-layer framework for name measurement. The form layer measures linguistic fluency using 38 Chinese-oriented features. The reference layer captures cultural meanings through human coding and large language model expansion with reliability evaluation. The relation layer measures name reuse, semantic crowding, and lexical variation. The three layers are largely independent, with correlations below 0.11. Census analysis reveals that name diversity follows Heaps' law, new-name adoption declines over time, name reuse shows heavy-tailed patterns, and repeated naming occurs at distinct creator-level and cross-creator time scales. Cultural events also trigger rapid naming responses. The framework, annotated dataset, and code are released to support scalable analysis of naming behavior in digital markets and other naming environments.
