跳到正文
研踪PaperTrail
← 研踪 PaperTrail

arXiv:2611.02657 · v1

Constructing an Artworld Influencers Network by Mining Social Media

首次提交 2026-11-02 15:16
版本更新 2026-11-02 15:16
雷达观测 2026-11-04 23:42
arXiv ↗PDF ↗

摘要

The author shows a methodology for constructing a network map of the artworld. The tweets posted by a manually comprised set of exemplar artworld actors and Natural Language Processing (NLP) methods are used to identify significant artworld actors, including those that do not own a Twitter account. Identified artworld actors form communities detected with the Louvain method, and their authority is evaluated with the HITS centrality method. The resulting artworld network exhibits the ability, starting with a small set of exemplar actors, to identify new artworld actors and provides a wealth of data that would be very hard to collect otherwise.

作者备注:Author's accepted manuscript. Published in Leonardo 55(1), 24-29 (2022), MIT Press. (c) 2022 ISAST. Supplemental material and tweet IDs: https://doi.org/10.5281/zenodo.23586408

期刊信息:Leonardo 55(1), 24-29 (2022)

AI 论文解读

基于摘要 · 非全文审阅

此版本尚未生成 AI 解读。解读由后台任务生成并按论文版本保存,登录后共享已有结果。

版本差异

目前只观测到一个版本,无法比较变化。之后收录新版本时,会在这里对照标题、作者、分类和摘要。

观测到的版本

相邻记录可能跳过中间版本;首次发现 v2 及以上也会标为版本更新。

v1 · 2026-11-02 15:16 · 最新观测版本

Constructing an Artworld Influencers Network by Mining Social Media

The author shows a methodology for constructing a network map of the artworld. The tweets posted by a manually comprised set of exemplar artworld actors and Natural Language Processing (NLP) methods are used to identify significant artworld actors, including those that do not own a Twitter account. Identified artworld actors form communities detected with the Louvain method, and their authority is evaluated with the HITS centrality method. The resulting artworld network exhibits the ability, starting with a small set of exemplar actors, to identify new artworld actors and provides a wealth of data that would be very hard to collect otherwise.

查看此版本解读 →查看此版本原文 ↗