arXiv：cs.LG· Brinda Murali Krishna, Oktay Karaku\c{s}, Can Ey\"upo\u{g}lu·· 2 小时前
组织级语义身份的计算建模框架：基于 K-pop 歌词数据的实证研究
A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data
AI 导读
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"，用于从纵向文本中刻画组织级语义身份，并以 1997 至 2023 年间 4 家 K-pop 娱乐公司旗下 126 位艺人的 1,289 首歌曲进行检验。
正文
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.LG · arxiv.org