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Journal of Scientific Information Research

Keywords

evolution of disciplinary topic, topic evolution analysis, co-word analysis, citation analysis, topic model

Abstract

[Purpose/significance] Starting from the related concepts of disciplinary topic evolution, this study comprehensively examines the forms and methods of disciplinary topic evolution. It identifies problems with existing concepts and shortcomings in current methods, and suggests directions for improvement, providing a reference for future research in this area.

[Method/process] Using a systematic review method, the literature on disciplinary topic evolution from the CNKI and Web of Science, as well as other academic platforms, was analyzed. The study defines related concepts, summarizes the forms of disciplinary topic evolution, and systematically reviews the current state of research on methods for disciplinary topic evolution. It analyzes existing methods from different perspectives: word frequency based methods, co-word-based methods, citation-based methods, and topic model-based methods, summarizing their respective advantages and disadvantages.

[Result/conclusion] This study defines the concept of disciplinary topic evolution, clarifies the forms of disciplinary topic evolution, and summarizes the gaps in existing research, such as the unclear definition of the concept, non-standard evolution forms, single data sources and the limited diversity of analytical methods. Finally, it proposes future directions, including clarifying the connotation and extension of disciplinary topic evolution, standardizing its forms, utilizing multi-source data as data sources, and actively integrating different methods for topic evolution analysis.

First Page

59

Last Page

69

Submission Date

21-Oct-2024

Revision Date

05-Dec-2024

Acceptance Date

16-Jan-2025

Published Date

01-Jul-2025

Reference

[1] 吴志祥, 赵凯蕊, 何超, 等. 学科领域主题演化与预测研究: 理论、方法与价值[J]. 情报理论与实践, 2022, 45 (6): 98-105.
[2] 王燕鹏. 国内基于主题模型的科技文献主题发现及演化研究进展[J]. 图书情报工作, 2016, 60 (3): 130-137.
[3] 赵迎光, 洪娜, 安新颖. 主题模型在主题演化方法中的应用研究进展[J]. 现代图书情报技术, 2014, 30 (10): 63-69.
[4] ELI, COHEN B, SCOTT, et al. Disciplinary Evolution and the Rise of the Transdiscipline[J]. Informing science: The International Journal of an Emerging Transdiscipline, 2014, 17: 189-215.
[5] HAN W T, HAN X, ZHOU S J, et al. The development history and research tendency of medical informatics: topic evolution analysis[J]. JMIR Medical Informatics, 2022, 10 (1): e31918.
[6] DWIVEDI R, ELLURI L. Exploring Generative Artificial Intelligence Research: A Bibliometric Analysis Approach[J]. IEEE Access, 2024, 12: 119884-119902.
[7] COCCIA M, ROSHANI S. Evolution of topics and trends in emerging research fields: multipleanalyseswith entity linking, Mann-Kendall test and burst methods in cloud computing[J]. Scientometrics, 2024, 129 (9): 5347-5371.
[8] ZHOU H K, YU H M, HU R. Topic discovery and evolution in scientific literature based on content and citations[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18 (10): 1511-1524.
[9] TREVISANIM M, TUZZI A. Learning the evolution of disciplines from scientific literature: a functional clustering approach to normalized keyword count trajectories[J]. Knowledge-Based Systems, 2018 (15): 129-141.
[10] 汪雅喜. 基于共词网络的学科主题结构与演化研究: 以知识管理学科领域为例[D]. 杭州: 浙江理工大学, 2013.
[11] 吕品. 基于社团结构动态演化的主题突变监测研究[D]. 南京: 南京理工大学, 2018.
[12] 潘高枝. 多源科技文献的主题识别、演化与时滞分析: 以隐私领域为例[D]. 南京: 南京信息工程大学, 2023.
[13] 王莉亚. 主题演化研究进展[J]. 情报探索, 2014 (4): 29-32.
[14] 王春秀, 冉美丽. 学科主题演化定量分析的理论基础探析[J]. 现代情报, 2008 (6): 48-50.
[15] 沈思, 王东波, 张祥, 等. 基于文献时间特征的学科主题演化分析方法研究: 以图书情报学领域为例[J]. 图书情报工作, 2013, 57 (7): 81-88.
[16] 韩真. 基于共词分析的主题类型划分方法比较研究[J]. 图书馆, 2009 (2): 46-47, 53.
[17] 钱旦敏, 郑建明. 基于LDA主题模型的信息服务文献主题提取与演变研究[J]. 数字图书馆论坛, 2019 (10): 16-22.
[18] 茅利锋. 基于主题模型的主题演化分析及预测[D]. 南京: 南京邮电大学, 2016.
[19] 孟菊园. 科学主题演化与预测研究: 以合成生物学为例[D]. 北京: 北京理工大学, 2018.
[20] 梁爽, 刘小平. 基于文本挖掘的科技文献主题演化研究进展[J]. 图书情报工作, 2022, 66 (13): 138-149.
[21] 刘自强, 王效岳, 白如江. 多维度视角下学科主题演化可视化分析方法研究: 以我国图书情报领域大数据研究为例[J]. 中国图书馆学报, 2016, 42 (6): 67-84.
[22] 霍朝光, 董克, 司湘云. 国内外LIS学科主题热度演化分析与预测[J]. 图书情报知识, 2021 (2): 35-47, 57.
[23] 刘自强. 基于NSF数据的研究前沿主题识别及演化规律研究[D]. 淄博: 山东理工大学, 2017.
[24] XU M Y, DU J P, XUE Z, et al. A scientific research topic trend prediction model based on multi-LSTM and graph convolutional network[J]. International Journal of Intelligent Systems, 2022, 37 (9): 6331-6353.
[25] CAI M, LUO H, CUI Y. A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies[J]. Complexity, 2021: 1-23.
[26] CALLON M, COURTIAL J, LAVILLE F. Co-word analysis as a tool for describing the network of interactions between basic and technological research: the case of polymerchemistry[J]. Scientometrics, 1991, 22 (1): 155-205.
[27] PHAM M, KLAMMA R, JARKE M. Development of computer science disciplines: a social network analysis approach[J]. Social Network Analysis and Mining, 2011, 1 (4): 321-340.
[28] 胡吉霞. 面向多源数据的学科知识网络构建方法与应用研究[D]. 西安: 西安电子科技大学, 2021.
[29] 白雅洁. 网络舆情领域研究的内容主题和交叉主题的识别及特征分析[D]. 太原: 山西财经大学, 2023.
[30] WANG Z Y, CHEN J, CHEN J P, et al. Identifying interdisciplinary topics and their evolution based on BERTopic[J]. Scientometrics, 2023, 129 (11): 7359-7384.
[31] MENG F Y, LU Z Y, LI X, et al. Demand-side energy management reimagined: A comprehensive literature analysis leveraging large language models[J]. Energy, 2024, 291 (C) .
[32] 傅柱, 王曰芬, 陈必坤. 国内外知识流研究热点: 基于词频的统计分析[J]. 图书馆学研究, 2016 (14): 2-12, 21.
[33] 储节旺, 钱倩. 基于词频分析的近10年知识管理的研究热点及研究方法[J]. 情报科学, 2014, 32 (10): 156-160.
[34] 柯平, 宫平. 数字人文研究演化路径与热点领域分析[J]. 中国图书馆学报, 2016, 42 (6): 13-30.
[35] 吕一博, 韦明, 林歌歌. 基于专利计量的技术融合研究: 判定、现状与趋势: 以物联网与人工智能领域为例[J]. 科学学与科学技术管理, 2019, 40 (4): 16-31.
[36] 高劲松, 彭博. 关键词频度演化视角下的研究热点挖掘方法研究[J]. 图书与情报, 2020 (3): 61-70.
[37] KLEINBERT J. Bursty and hierarchical structure in streams[J]. Data Mining and Knowledge Discovery, 2002, 7 (4): 91-101.
[38] 郑乐丹. 基于突变检测的学科领域新兴研究趋势探测分析[J]. 情报杂志, 2012, 31 (9): 50-53.
[39] CHEN C M. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the Association for Information Science and Technology, 2006, 57 (3): 359-377.
[40] 姜鑫, 王德庄, 马海群. 关键词词频变化视角下我国“科学数据”领域研究主题演化分析[J]. 现代情报, 2018, 38 (1): 141-146, 161.
[41] 唐果媛, 张薇. 基于共词分析法的学科主题演化研究进展与分析[J]. 图书情报工作, 2015 (5): 128-136.
[42] 王晓光, 程齐凯. 基于NEViewer的学科主题演化可视化分析[J]. 情报学报, 2013, 32 (9): 900-911.
[43] 李干, 袁勤俭, 舒小昀, 等. 国际E-Learning研究热点演化及趋势探测[J]. 现代远程教育研究, 2015 (4): 41-52, 103.
[44] 刘艳华, 华薇娜, 钱爱兵. 国际健康信息研究主题的演化路径分析[J]. 现代情报, 2018, 38 (7): 135-141.
[45] 陈淋, 屈文建. 基于共词分析的我国图书情报学研究主题演化分析[J]. 新世纪图书馆, 2017 (12): 13-18.
[46] 王康, 高继平, 潘云涛, 等. 多位态研究主题识别及其演化路径方法研究[J]. 图书情报工作, 2021, 65 (11): 113-122.
[47] 唐果媛. 基于共词分析法的学科主题演化研究方法的构建[J]. 图书情报工作, 2017, 61 (23): 100-107.
[48] 程齐凯, 王晓光. 一种基于共词网络社区的科研主题演化分析框架[J]. 图书情报工作, 2013, 57 (8): 91-96.
[49] HUANG C, YANG C, WANG S T, et al. Evolution of topics in education research: A systematic review using bibliometric analysis[J]. Educational Review, 2020, 72 (1): 1-17.
[50] 洪勇, 张红虹. 基于共词分析的国外新兴产业研究知识网络分析[J]. 技术经济, 2015, 34 (1): 5-12.
[51] 赵蓉英, 田沛霖, 常茹茹. 数据科学研究的主题关联结构与发展演化态势: 共词网络视角[J]. 图书馆学研究, 2021 (11): 2-12.
[52] 程秀峰, 邹晶晶, 叶光辉, 等. 融合Word2Vec的半积累引用共词网络的领域主题演化研究[J]. 情报学报, 2023, 42 (7): 801-815.
[53] WANG X G, HE J, HUANG H, et al. MatrixSim: A new method for detecting the evolution paths of research topics[J]. Journal of Informetrics, 2022, 16 (4), 101343.
[54] 黄晓斌, 吴高. 学科领域前沿探测方法研究述评[J]. 情报学报, 2019, 38 (8): 872-880.
[55] 孙月瑞. 基于多元数据融合的学科文献主题识别及演化分析研究[D]. 杭州: 杭州电子科技大学, 2023.
[56] 赵蓉英, 王建品. 引用内容分析与引文著录分析的比较研究[J]. 图书情报工作, 2017, 61 (10): 110-115.
[57] GARFIELD E. Historiographic mapping of knowledge domains literature[J]. Journal of Information Science, 2004, 30 (2): 119-145.
[58] 罗双玲, 张文琪, 夏昊翔. 基于半积累引文网络社区发现的学科领域主题演化分析: 以“合作演化”领域为例[J]. 情报学报, 2017, 36 (1): 100-110.
[59] KAJIKAWA Y, YOSHIKAWA J, TAKEDA Y, et al. Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy[J]. Technological Forecasting and Social Change, 2008, 75 (6): 771-782.
[60] SHIBATA N, KAIJIKAWA Y, TAKEDA Y, et al. Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications[J]. Technological Forecasting and Social Change, 2011, 78 (2): 274-282.
[61] 赵党志. 共引分析: 研究学科及其文献结构和特点的一种有效方法[J]. 情报杂志, 1993, 12 (2): 36-42, 79.
[62] SMALL H. Co-citation in the scientific literature: A new measure of the relationship between two documents[J]. Journal of the American Society for information Science, 1973, 24 (4): 265-269.
[63] BRUER J. Can we talk?How the cognitive neuroscience of attention emerged from neurobiology and psychology, 1980-2005[J]. Scientometrics. 2010, 83 (3): 751-764.
[64] 郭彬, 杨振中. 基于SCI的科技创新领域文献同被引分析[J]. 现代情报, 2016, 36 (10): 164-170.
[65] 王灿友, 苏秦. 基于文献分析的3D打印技术的演化及发展前沿[J]. 情报杂志, 2015, 34 (9): 72-77, 71.
[66] 侴宇楠. 研究主题的动态演化及知识流动特性分析[D]. 哈尔滨: 哈尔滨工业大学, 2011.
[67] 罗式胜. 耦合类型与分析[J]. 图书情知识, 1985 (1): 42-47.
[68] 陈远, 王菲菲. 基于CSSCI的国内情报学领域作者文献耦合分析[J]. 情报资料工作, 2011 (5): 6-12.
[69] 肖明, 李国俊, 袁浩. 基于引文耦合的数字图书馆研究结构可视化分析[J]. 图书情报工作, 2010, 54 (7): 51-54, 149.
[70] LIU J S, LU L, LU W M. Research fronts in data envelopment analysis[J]. Omega, 2015, 58 (C): 33-45.
[71] 许振亮, 刘冬潇, 刘喜美. 基于Sci2工具包的国际创业研究文献引文耦合分析[J]. 信息资源管理学报, 2017, 7 (1): 56-69.
[72] 张艺蔓, 李秀霞, 韩牧哲. 基于引文耦合的情报学学科结构时序分析[J]. 情报杂志, 2015, 34 (3): 100-106.
[73] 徐戈, 王厚峰. 自然语言处理中主题模型的发展[J]. 计算机学报, 2011, 34 (8): 1423-1436．
[74] 胡吉明, 陈果. 基于动态LDA主题模型的内容主题挖掘与演化[J]. 图书情报工作, 2014, 58 (2): 138-142.
[75] HAN X Y. Evolution of research topics in LIS between 1996 and 2019: An analysis based on latent Dirichlet allocation topic model[J]. Scientometrics, 2020, 125 (3): 2561-2595.
[76] 侯倩倩. 基于文本挖掘的我国制氢领域热点主题识别与演化研究[D]. 太原: 太原科技大学, 2022.
[77] 马秀敏. 中国典型管理期刊文献主题发现与演化分析[D]. 大连: 大连理工大学. 2011.
[78] WANG X R, MCCALLUM A. Topics over time: a non-Markov continuous-time model of topical trends[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2006.
[79] 何伟林, 奉国和, 谢红玲. 基于CSToT模型的科技文献主题发现与演化研究[J]. 数据分析与知识发现, 2018, 2 (11): 64-72.
[80] GRIFFITHS T L, STEYVERS M. Finding scientific topics[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101 (Supplement 1): 5228-5235.
[81] WANG C, BLEI D, HECKERMAN D. Continuous Time Dynamic Topic Models[C]//Conference on Uncertainty in Artificial Intelligence. Helsinki, 2008.
[82] LU C Y, ZHU L J, XIE Y S, et al. Analysis of Hot Topics and Evolution of Research in World-class Agricultural Universities Based on BERTopic[J]. Applied Mathematics and Nonlinear Sciences, 2024, 9 (1): 1-10.
[83] BAGHERI R, ENTEZARIAN N, SHARIFI M H. Topic Modeling on System Thinking Themes Using Latent Dirichlet Allocation, Non-Negative Matrix Factorization and BERTopic[J]. Journal of Systems Thinking in Practice, 2023, 2 (4): 33-56.

Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2025.03.006

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