[1] TREPTE S, SCHARKOW M, DIENLIN T. The privacy calculus contextualized: the influence of affordances[J]. Computers in human behavior, 2020, 104: 106115.
[2] 中国互联网信息中心. 第55次《中国互联网络发展状况统计报告》—互联网发展研究[EB/OL]. [2025-04-08]. https://www.cnnic.net.cn/n4/2025/0117/c88-11229.html. (China Internet Network Information Center. The 55rd statistical report on Internet development in China - Internet development research[EB/OL]. [2025-04-08]. https://www.cnnic.net.cn/n4/2025/0117/c88-11229.html.)
[3] 魏静, 刘启凡, 朱恒民, 等. 网络社交平台媒体信息影响下人际社交网络个体观点演化研究[J]. 情报理论与实践, 2024, 47(2): 151-160. (WEI J, LIU Q F, ZHU H M, et al. Research on the evolution of individual opinions in interpersonal social networks under the influence of media information on online social platforms[J]. Information studies theory & application, 2024, 47(2): 151-160.)
[4] LIU J, LI T R, XIE P, et al. Urban big data fusion based on deep learning: an overview[J]. Information fusion, 2020, 53: 123-133.
[5] AUSSU P. Information overload: coping mechanisms and tools impact[C]//International conference on research challenges in information science. Cham: Springer Nature, 2023: 661-669.
[6] BASS T. Intrusion detection systems and multisensor data fusion[J]. Communications of the ACM, 2000, 43(4): 99-105.
[7] 周晓英, 裴俊良. 健康信息学的学科范畴与中国健康信息学的发展——兼述健康信息学学科建设与发展学术研讨会[J]. 中国图书馆学报, 2022, 48(2): 76-93. (ZHOU X Y, PEI J L. The discipline category of health informatics and the development of health informatics in China[J]. Journal of library science in China, 2022, 48(2): 76-93.)
[8] 戴萍萍. 移动社交媒体健康信息质量评价体系研究[D]. 长沙: 中南大学, 2023. (DAI P P. Research on health information quality evaluation system of mobile social media[D]. Changsha: Central South University, 2023.)
[9] 李月琳, 张秀, 王姗姗.社交媒体健康信息质量研究: 基于真伪健康信息特征的分析[J]. 情报学报, 2018, 37(3): 294-304. (LI Y L, ZHANG X, WANG S S. Health information quality in social media: an analysis based on the features of real and fake health information [J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(3): 294-304.)
[10] 金晓玲, 冯慧慧, 周中允. 微信朋友圈中健康信息传播行为研究[J]. 管理科学, 2017, 30(1): 73-82. (JIN X L, FENG H H, ZHOU Z R, et al. An empirical study on healthcare information diffusion behavior in Wechat moments [J]. Journal of management science, 2017, 30(1): 73-82.)
[11] 付少雄, 孙岚, 邓胜利, 等.应对理论视角下短视频用户虚假健康信息采纳的动因研究[J]. 情报资料工作, 2023, 44(6): 100-110. (FU S X, SUN L, DENG S L, et al. A study on the motivation of short video users' false health information adoption from the perspective of coping theory[J]. Information and documentation services, 2023, 44(6): 100-110.)
[12] 彭丽徽, 蒋欣.风险认知视域下社交媒体用户健康信息规避行为生成机理研究[J]. 图书情报工作, 2022, 66(22): 55-65. (PENG L H, JIANG X. A study on the generation mechanism of social media users' health information avoidance behaviors under the risk perception perspective[J]. Library and information service, 2022, 66(22): 55-65.)
[13] 高靖超, 彭丽徽, 张艳丰, 等. 在线医疗社区健康焦虑用户画像模型构建及实证研究[J]. 图书情报工作, 2023, 67(16): 124-134. (GAO J C, PENG L H, ZHANG Y F, et al. Construction and empirical research of health anxiety user portrait model in online medical community[J]. Library and information service, 2023, 67(16): 124-134.)
[14] 王文韬, 李晶, 张帅, 等. 基于不完全信息动态博弈模型的电子健康网站用户信任机制研究[J]. 图书情报知识, 2018(3): 78-84. (WANG W T, LI J, ZHANG S, et al. Research on user trust mechanism of electronic health websites based on the incomplete information dynamic game model[J]. Documentation, information & knowledge, 2018(3): 78-84.)
[15] 岑咏华, 王曰芬. 学科主题探测与演化分析: 多视角述评与展望[J]. 情报学报, 2023, 42(4): 477-494. (CEN Y H, WANG Y F. Recent advancements in detection and evolutionary tracking of scientific topics: a multi-perspective survey and prospect[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(4): 477-494.)
[16] CHEN B T, TSUTSUI S, DING Y, et al. Understanding the topic evolution in a scientific domain: an exploratory study for the field of information retrieval[J]. Journal of informetrics, 2017, 11(4): 1175-1189.
[17] 谭春辉, 熊梦媛. 基于LDA模型的国内外数据挖掘研究热点主题演化对比分析[J]. 情报科学, 2021, 39(4): 174-185. (TAN C H, XIONG M Y. Contrastive analysis at home and abroad on the evolution of hot topics in the field of data mining based on LDA model[J]. Information science, 2021, 39(4): 174-185.)
[18] 丁晟春, 刘笑迎, 李真. 融合评论影响力的网络舆情热点主题演化研究[J]. 现代情报, 2021, 41(8): 87-97. (DING S C, LIU X Y, LI Z. Research on the evolution of hot topics of online public opinion with the influence of comments[J]. Journal of modern information, 2021, 41(8): 87-97.)
[19] 曹树金, 曹茹烨. 基于研究主题和引文分析的信息资源管理学科发展探究[J]. 信息资源管理学报, 2023, 13(2): 12-29. (CAO S J, CAO R Y. Research on the development of the first-level discipline of information resource management based on research theme and citation analysis[J]. Journal of information resources management, 2023, 13(2): 12-29.)
[20] 祝娜, 王芳. 基于主题关联的知识演化路径识别研究——以3D打印领域为例[J]. 图书情报工作, 2016, 60(5): 101-109. (ZHU N, WANG F. Identification of knowledge evolutionary path based on topic relevance: taking the case of 3D printing field[J]. Library and information service, 2016, 60(5): 101-109.)
[21] 韩亚楠, 刘建伟, 罗雄麟. 概率主题模型综述[J]. 计算机学报, 2021, 44(6): 1095-1139. (HAN Y N, LIU J W, LUO X L. A survey on probabilistic topic model[J]. Chinese journal of computers, 2021, 44(6): 1095-1139.)
[22] 王金丽. 科技文献热点主题提取及演化研究[D]. 昆明: 昆明理工大学, 2021. (WANG J L. Research on extraction and evolution of hot topics in scientific literature[D]. Kunming: Kunming University of Science and Technology, 2021.)
[23] 韩肖赟, 侯再恩, 孙绵. 主题模型在短文本上的应用研究[J]. 计算机工程与科学, 2020, 42(1): 144-152. (HAN X Y, HOU Z E, SUN M. Research on the application of topic model in short text[J]. Computer engineering & science, 2020, 42(1): 144-152.)
[24] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet Allocation[J]. Journal of machine Learning research, 2003, 3: 993-1022.
[25] BLEI D M, KUCUKELBIR A, MCAULIFFE J D. Variational inference: a review for statisticians[J]. Journal of the American Statistical Association, 2017, 112(518): 859-877.
[26] MACQUEEN J. Some methods for classification and analysis of multivariate observations[C]//Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Los Angeles: University of California Press, 1967, 1(14): 281-297.
[27] MUSTAFI D, SAHOO G. A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the k-means algorithm with applications in text clustering[J]. Soft computing, 2019, 23: 6361-6378.
[28] 李贺, 谷莹, 刘嘉宇. 数据驱动下基于语义相似性的产品需求识别研究[J]. 情报理论与实践, 2022, 45(5): 99-106. (LI H, GU Y, LIU J Y. Research on data driven product demand recognition based on semantic similarity[J]. Information studies theory & application, 2022, 45(5): 99-106.)
[29] 夏火松, 李保国, 杨培. 基于改进K-means聚类的在线新闻评论主题抽取[J]. 情报学报, 2016, 35(1): 55-65. (XIA H S, LI B G, YANG P. Topic extraction in news comments based on improved K-means clustering algorithm[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(1): 55-65.)
[30] SILVA C, RIBEIRO B. On text-based mining with active learning and background knowledge using SVM[J]. Soft computing, 2007, 11: 519-530.
[31] 胡泽文, 任萍, 崔静静. 基于机器学习模型的科技论文潜在“精品”识别研究[J]. 情报学报, 2023, 42(2): 189-202. (HU Z W, REN P, CUI J J. Study on identification of potential "treasures" in massive papers based on machine learning models[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(2): 189-202.)
[32] 姜艺, 黄永, 夏义堃, 等. 学术文本词汇功能识别—在关键词自动抽取中的应用[J]. 情报学报, 2021, 40(2): 152-162. (JIANG Y, HUANG Y, XIA Y K, et al. Recognition of lexical functions in academic texts: application in automatic keyword extraction[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(2): 152-162.)
[33] DEY R, ROY A, CHAKRABORTY T, et al. Sleeping beauties in computer science: characterization and early identification[J]. Scientometrics, 2017, 113: 1645-1663.
[34] 常耀成, 张宇翔, 王红, 等. 特征驱动的关键词提取算法综述[J]. 软件学报, 2018, 29(7): 2046-2070. (CHANG Y C, ZHANG Y X, WANG H, et al. Features oriented survey of state-of-the-art key phrase extraction algorithms[J]. Journal of software, 2018, 29(7): 2046-2070)
[35] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013.
[36] 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. (CHENG X F, ZOU J J, YE G H, et al. Exploring the evolution of research topics based on partial-cumulative citation co-word network with Word2Vec[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(7): 801-815.)
[37] 卢恒, 陈章杰, 周知. 基于知识图谱的虚拟学术社区用户生成内容知识共聚框架研究[J]. 情报理论与实践, 2023, 46(12): 157-166, 192. (LU H, CHEN Z J, ZHOU Z. Research on knowledge copolymerization framework of user generated content in virtual academic community based on knowledge graph[J]. Information studies theory & application, 2023, 46(12): 157-166, 192.)
[38] 刘江峰, 王希羽, 张君冬, 等. 领域文献深层语义特征视角下的期刊新兴研究主题发现[J]. 情报理论与实践, 2024, 47(3): 177-187. (LIU J F, WANG X Y, ZHANG J D, et al. Emergent research topic discovery in journals from the perspective of deep semantic features of domain literature[J]. Information studies theory & application, 2024, 47(3): 177-187.)
[39] GROOTENDORST M. BERTopic: neural topic modeling with a class-based TF-IDF procedure[J]. arXiv preprint arXiv:2203.05794, 2022.
[40] 梁继文, 杨建林, 王伟. 知识单元重组视角下的科学主题预测研究[J]. 情报学报, 2023, 42(5): 511-524. (LIANG J W, YANG J L, WANG W. Research on scientific topic prediction from the perspective of knowledge unit reorganization[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(5): 511-524.)
[41] 傅予, 曹屹楠, 王强. 基于图文多维特征融合的移动社交媒体用户生成内容信息采纳影响机制研究[J]. 情报资料工作, 2024, 45(1): 55-67. (FU Y, CAO Q N, WANG Q. Research on the influence mechanism of mobile social media User-Generated Content information adoption based on multidimensional feature fusion of images and texts[J]. Information and documentation services, 2024, 45(1): 55-67.)
[42] 滕广青, 江瑶, 庹锐. 基于多数据源维度的领域知识演化对比研究: 以美国石墨烯领域研究为例[J]. 情报资料工作, 2023, 44(6): 61-70. (TENG G Q, JIANG Y, TUO R. Comparative study on domain knowledge evolution based on multiple data source dimensions: taking graphene field research in the United States as an example[J]. Information and documentation services, 2023, 44(6): 61-70.)
[43] 杨思洛, 吴丽娟. 基于BERTopic模型的国外信息资源管理研究进展分析[J]. 情报理论与实践, 2024, 47(2): 189-197. (YANG S L, WU L J. Research progress of foreign information resource management: an analysis based on the BERTopic model[J]. Information studies theory & application, 2024, 47(2): 189-197.)
[44] 赵爽, 周桂君. 中华哲学典籍海外读者评价的影响因素研究——以《易经》英译本为例[J]. 现代情报, 2023, 43(3): 64-72, 147. (ZHAO S, ZHOU G J. Research on the influencing factors of overseas readers' evaluation of Chinese philosophical classics: the English translations of the Book of Changes as an example[J]. Journal of modern information, 2023, 43(3): 64-72, 147.)
[45] 马费成, 张帅. 我国图书情报领域新兴交叉学科发展探析[J]. 中国图书馆学报, 2023, 49(2): 4-14. (MA F C, ZHANG S. The development of emerging interdisciplines in library and information science in China[J]. Journal of library science in China, 2023, 49(2): 4-14.)
[46] LI J N, LI D X, XIONG C M, et al. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation[C]//International conference on machine learning. Nederland: ML Research Press, 2022: 12888-12900.
[47] SUN Z J, LI X Y, SUN X F, et al. Chinesebert: Chinese pretraining enhanced by glyph and pinyin information[J]. arXiv preprint arXiv:2106.16038, 2021.
[48] RABINER L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE, 1989, 77(2): 257-286.
[49] 蔡婷婷, 朱恒民, 魏静. 基于HMM的话题风险状态预测方法研究[J]. 计算机技术与发展, 2023, 33(5): 29-34. (CAI T T, ZHU H M, WEI J. Research on prediction method of topic risk states based on HMM[J]. Computer technology and development, 2023, 33(5): 29-34.)
[50] 陈伟, 林超然, 李金秋, 等. 基于LDA-HMM的专利技术主题演化趋势分析——以船用柴油机技术为例[J]. 情报学报, 2018, 37(7): 732-741. (CHEN W, LIN C R, LI J Q, et al. Evolution trend analysis of patent technology theme based on LDA-HMM: a case study of Marine diesel engine technology[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(7): 732-741.)
[51] Bilibili[EB/OL]. [2025-04-08]. https://ir.bilibili.com/cn/.
[52] 朱梦蝶, 付少雄, 程为, 等. MOA视角下在线视频社区用户自我披露行为研究——以bilibili为例[J]. 图书与情报, 2023(5): 51-61. (ZHU M D, FU S X, CHENG W, et al. Research on the influencing factors of users' self-disclosure behavior in online video communities from the perspective of MOA theory: taking Bilibili as an example[J]. Library & information, 2023(5): 51-61.)
[53] 商琦, 陈洪梅. 区块链技术创新态势专利情报实证[J]. 情报杂志, 2019, 38(4): 23-28, 59. (SHANG Q, CHEN H M. Empirical analysis of the patent intelligence on the innovation status of blockchain technology[J]. Journal of intelligence, 2019, 38(4): 23-28, 59.)
[54] ECK N J, WALTMAN L. How to normalize cooccurrence data? An analysis of some well‐known similarity measures[J]. Journal of the American Society for Information Science and Technology, 2009, 60(8): 1635-1651.
