我国生成式人工智能研究的热点、作者与期刊

• 作者： 孙安 ¹ 沈东潮 ¹ 陈涛 ²
• 作者单位：
  1.  河南科技大学图书馆

  2.  中山大学信息管理学院
• 提交时间：2024-09-06

摘要: 共被引分析是一种揭示领域知识结构的重要方法。生成式人工智能（AIGC）受到各界关注并获得广泛研究，对国内AIGC领域发文成果进行共被引分析和结果解读，揭示AIGC研究的热点和发展趋势。由于目前国内知识门户如知网、万方等缺乏导出文献引文数据能力。本研究综合利用Selenium、Python和大语言模型实现一键式引文数据采集，自动化完成数据处理、重构和合并。并针对“生成式人工智能”研究领域，借助CiteSpace开展文献共被引、作者共被引和期刊共被引分析研究。当前我国AIGC研究热点主要集中于法律风险与伦理挑战，教育领域的创新与变革、智能传播环境下的机遇和挑战，以及图书馆智慧知识服务；核心作者主要集中于法学领域；核心期刊主要涉及法学、新闻传播学、教育学、信息资源管理等学科领域。同时本研究相关代码已在Gitee开源，对推动国内共被引分析研究具有一定的现实意义。

Abstract: Co-citation analysis is a crucial method for uncovering the knowledge structure within a field. Artificial Intelligence Generated Content (AIGC) has garnered significant attention and has been the focus of extensive research. This study conducts a co-citation analysis of research outputs in China’s AIGC field, providing a detailed interpretation of the results to identify research hot spots and development trends in AIGC. Given the current lack of citation data export capabilities in domestic knowledge portals such as CNKI and Wanfang, this study employed Selenium, Python, and large language models to achieve one-click citation data collection, automating the processes of data processing, reconstruction, and merging. Additionally, in the AIGC research area, empirical studies were performed using CiteSpace to conduct co-citation analyses of documents, authors, and journals. Current research hot-spots in China’s AIGC field predominantly focus on legal risks and ethical challenges, innovation and transformation in education, opportunities and challenges within the intelligent communication environment, and smart knowledge services in libraries. Core authors are concentrated in the legal domain, while core journals are primarily associated with law, communication, education, and information resource management. Furthermore, the related code from this study has been open-sourced on Gitee, contributing to the advancement of co-citation analysis research in China.

• 来自： 沈东潮
• 分类： 信息资源管理 >> 情报学 信息资源管理 >> 图书馆学
• 稿件状态： 已投稿期刊
• 引用： PSSXiv:202409.00545 (或此版本 PSSXiv:202409.00545V1)
  DOI:10.12451/202409.00545
  CSTR:32012.36.PSSXiv.202409.00545
• 推荐引用方式： 孙安,沈东潮,陈涛.我国生成式人工智能研究的热点、作者与期刊.哲学社会科学预印本平台:https://zsyyb.cn/abs/202409.00545.[PSSXiv:202409.00545V1] (点此复制)