arXiv cs.CL· Brinda Murali Krishna, Oktay Karaku\c{s}, Can Ey\"upo\u{g}lu·· 3 天前AI 评分15
从纵向文本数据建模组织级语义身份的计算框架
A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data
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研究者提出一个计算社会科学框架，将语义与图度量结合为可解释的组织指纹和时间画像，用于描述组织关联文本的集体语义分化与变化。基于1997至2023年间四家K-pop娱乐公司126位艺人的1,289首歌曲，仅三项新颖性度量在艺人层面呈显著差异，多样性、连通性度量及公司—社区富集大多不显著；艺人分组平衡准确率为0.334（随机基线0.25）。
正文 · 原文
Abstract:Can longitudinal texts produced by organisationally affiliated contributors reveal collective semantic differentiation and change without obscuring differences among those contributors? We develop a computational social science framework that combines established semantic and graph measures into interpretable organisational fingerprints and temporal profiles. We use \emph{organisation-level semantic identity} to describe a corpus-dependent interpretation of recurring and changing features of affiliated textual output, rather than corporate culture or managerial intention. Drawing on an inherited semantic landscape, we examine 1,289 songs by 126 artists affiliated with four K-pop entertainment companies between 1997 and 2023. The evidence for differentiation is selective. After correction, all three novelty-related measures show artist-level differences, whereas the diversity and connectivity measures tested do not; only two company--community enrichments remain significant. Temporal trajectories reveal further distinctions between portfolios. Artist-grouped balanced accuracy reaches 0.334 against a chance baseline of 0.25, providing modest supporting evidence of affiliation-related signal rather than independent validation of identity. Robustness analyses support the presence of continuous semantic structure more strongly than precise rankings. Together, these findings show how collective regularities can coexist with shared content, contributor heterogeneity and change over time, while leaving cross-domain generality and broader organisational construct validity unresolved.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.29584 [cs.LG] |
| (or arXiv:2609.29584v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29584 arXiv-issued DOI via DataCite |
Submission history
From: Oktay Karakus Dr [view email]
[v1]
Thu, 27 Aug 2026 11:47:59 UTC (2,272 KB)
[v2]
Wed, 14 Oct 2026 13:34:11 UTC (2,245 KB)
来源：arXiv cs.CL · arxiv.org