图书情报工作

图书情报工作 >

2023 , Vol. 67 >Issue 20: 45 - 57

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.20.005

理论研究

数字人文视角下文化资源数字化开发和传播要素与影响机理研究

  • 傅予 ,
  • 李博然 ,
  • 徐拥军
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  • 1 中国人民大学信息资源管理学院 北京 100872;
    2 中国人民大学数字人文研究院 北京 100872;
    3 北京市人文北京研究基地 北京 100872
傅予,讲师,博士,硕士生导师;李博然,本科生。

收稿日期: 2023-03-14

  修回日期: 2023-07-20

  网络出版日期: 2023-11-08

基金资助

本文系国家社会科学基金重大项目“文化遗产智慧数据资源建设与服务研究”（项目编号：21&ZD3359）和国家自然科学基金项目“基于计算社会科学范式的旅游舆情自动内容分析方法与应用研究：以旅游危机沟通为例”（项目编号：72004224）研究成果之一。

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Discovering Key Factors and Influencing Mechanisms of Digital Development and Dissemination of Cultural Resources from the Perspective of Digital Humanities

  • Fu Yu ,
  • Li Boran ,
  • Xu Yongjun
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  • 1 School of Information Resource Management, Renmin University of China, Beijing 100872;
    2 Institute of Digital Humanities, Renmin University of China, Beijing 100872;
    3 Research Center for Humanistic Beijing, Beijing 100872

Received date: 2023-03-14

  Revised date: 2023-07-20

  Online published: 2023-11-08

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摘要

[目的/意义] 科学有效地进行文化资源数字化开发和传播依赖于对其开发和传播要素与影响机理的准确把握。从数字人文视角关注数字化开发传播的深层效果，对文化信息分析方法进行改进适配，以实现数字化开发传播要素和深层效果度量在文化大数据中的自动精确提取，进而系统性探究深层与表层效果影响机理的异同。[方法/过程] 从数字人文视角出发提出文化资源数字化开发和传播要素与影响机理的“双层效果”分析框架，进而从文化信息分析的三个阶段进行方法改进。首先，提出“雪球合成”信息全量获取方法；其次，构建基于大规模深度神经网络的要素和效果度量的自动提取模型；最后，对数字化开发和传播效果影响机理进行系统计量。[结果/结论] 自动提取模型的Accuracy、Precision、Recall和F-Measure性能指标均达到0.965以上；计量结果发现，影响深层效果的关键要素及影响机理相较于表层效果差异显著，存在部分共性影响要素但影响权重不同，此外各影响要素呈现出显著的交互作用，可以为文化资源数字化开发和传播实践提供科学参考和有力支撑。

关键词： 文化数字化; 数字人文; 文化大数据; 文化信息分析; 深度学习

本文引用格式

傅予 , 李博然 , 徐拥军 . 数字人文视角下文化资源数字化开发和传播要素与影响机理研究[J]. 图书情报工作, 2023 , 67(20) : 45 -57 . DOI: 10.13266/j.issn.0252-3116.2023.20.005

Abstract

[Purpose/Significance] Scientific and effective digital development and dissemination of cultural resources rely on an accurate understanding of the factors and influencing mechanisms. Based on the perspective of digital humanities, this paper focuses on the deep effect of digital development and dissemination. In order to automatically and accurately extract the measurements of the factors and deep effect in cultural big data, this paper improves and adapts cultural information analysis, and further systematically explores the similarities and differences between the influencing mechanisms for deep and superficial effects. [Method/Process] Based on the perspective of digital humanities, it proposed a “two-level effect” analysis framework of the factors and influencing mechanisms and made methodological improvements in three stages of cultural information analysis. Firstly, the “snowball synthesis” information acquisition was proposed to obtain full information. Secondly, automated extraction models based on a large-scale deep neural network were constructed for factor and effect measurements. Finally, the influencing mechanisms of digital development and dissemination effect are systematically quantified. [Result/Conclusion] The performance indicators of the automatic extraction model, including Accuracy, Precision, Recall, and F-Measure are all above 0.965. The empirical results reveal significant differences in the key factors and influencing mechanisms for the deep effect compared with the superficial one, finds some common key factors with different influence weights. In addition, the influencing factors show significant interaction effects. This study can provide scientific reference and strong support for the digital development and dissemination practice of cultural resources.

Key words： cultural digitization; digital humanities; cultural big data; cultural information analysis; deep learning

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