A Three-Layer Framework for Measuring Names and Its Census Application on a Token Launchpad

2026-09-29

AI Summary

This study addresses the lack of standardized metrics, inadequate Chinese language processing, and challenges in scalable cultural semantic analysis within digital asset naming. To this end, we construct a dataset comprising 510,000 Chinese names and propose a three-tier analytical framework encompassing formal, referential, and relational layers. Methodologically, we introduce the first 38-dimensional feature measurement system tailored for Chinese, integrated with large language models to automate cultural semantic encoding. Our analysis reveals several key patterns: naming diversity follows Heaps’ law, new-name adoption rates decay over time, name reuse exhibits heavy-tailed distributions, and cultural events drive rapid naming responses. All data and code are publicly available.

Abstract

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.meme 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.

Problem

• name measurement
• processing fluency
• cultural meaning
• semantic crowding
• token launchpad

Innovation

• Three-layer framework
• Chinese processing fluency
• Large language models
• Name competition
• Token launchpad