北京大学信息管理系成立75周年学术专辑

网络分析在数字人文中的应用框架

  • 王林旭 ,
  • 王军
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  • 1. 北京大学信息管理系 北京 100871;
    2. 北京大学数字人文研究中心 北京 100871
王林旭,博士研究生。

收稿日期: 2022-07-29

  修回日期: 2022-09-05

  网络出版日期: 2022-11-17

基金资助

本文系国家自然科学基金国际重点合作项目"中国儒家学术史知识图谱构建研究"(项目编号:72010107003)研究成果之一。

Application Framework of Network Analysis in Digital Humanities

  • Wang Linxu ,
  • Wang Jun
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  • 1.Department of Information Management, Peking University, Beijing 100871;
    2. The Center for Digital Humanities, Peking University, Beijing 100871

Received date: 2022-07-29

  Revised date: 2022-09-05

  Online published: 2022-11-17

摘要

[目的/意义] 网络分析作为有效的分析手段与可视化方式,是数字人文领域应用最广泛的方向之一。对网络分析在数字人文领域的应用进行系统总结与归纳,有助于数字人文研究人员快速明确网络分析的能力与局限,以便开展更深层次的研究实践。[方法/过程] 采用内容分析法对发表在国际数字人文领域最有影响力的期刊和国际数字人文会议上近5年的文章进行梳理与总结,从研究问题、数据集、网络特性、网络分析指标4个方面进行梳理,最终提炼出网络分析方法在数字人文领域的应用框架。[结果/结论] 该应用框架包括三类数据规模(单一文本、平行文本和语料库)、五大应用场景(角色分析网络、人物关联网络、话语空间网络、文本关联网络、文化主题网络)、两种指标规模(全局指标和局部指标)以及五类指标类型(构成、密度、中心度、派系和结构)。

本文引用格式

王林旭 , 王军 . 网络分析在数字人文中的应用框架[J]. 图书情报工作, 2022 , 66(20) : 50 -61 . DOI: 10.13266/j.issn.0252-3116.2022.20.006

Abstract

[Purpose/Significance] Network analysis is an effective analysis method and visualization method, is one of the most widely used directions in the field of digital humanities. A systematic summary and induction of the application of network analysis in the field of digital humanities will help the digital humanities researchers quickly identify the capabilities and limitations of network analysis, and to carry out deeper research practice. [Method/Process] In this study, we adopted content analysis to summary the papers published in the most influential journals in the international digital humanities field and International Digital Humanities Conference in the past five years. And from the five perspectives of research question, dataset, network special characteristics and network analysis metrics, the organization system for the application of network analysis methods in the field of digital humanities was finally extracted. [Result/Conclusion] The organizational structure includes three kinds of data scale (including single text analysis, parallel text analysis and corpus analysis), five application scenarios (containing characters analysis network, people relation network, words semantic network, text relevancy network and culture topic network), two scales of indicators (including global indicators and local indicators), and five types of metrics (like composition, density, centrality, clique and structure).

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