[Purpose/significance] Exploring the topic and evolution trend of hypertension literature is of great significance for users to understand the profile, research hot-spots and frontiers of chronic disease, and can promote the knowledge communication among experts.[Method/process] This paper takes the Hypertension and 26717 articles from PubMed database as the research object, extracts high-frequency Mesh Terms to construct a co-occurrence matrix. Social network analysis is applied to detect the community and topic distribution of the hypertension study literature, and the expanded topic modeling Dirichlet-multinomial regression is also used to explore the topic distribution and evolution trends. Then similarities and differences of the SNA and DMR method in topic detection are analyzed.[Result/conclusion] It is found that the hypertension literature is mainly concentrated on three communities, which can be divided into five research topics, such as risk factors, research methods, basic situation of patients, diagnosis and treatment, and animal experiments. The relative distribution of the topic varies with time change. It is also found that the topic obtained from SNA and DMR are basically similar. But the Mesh Terms obtained from SNA method are more specific and clearer, while the DMR is more broadly and have an advantage in exploring the evolution of various themes.
[1] 中国高血压防治指南修订委员会. 中国高血压防治指南2010[J]. 中华心血管病杂志, 2011, 39(7):701-708.
[2] NATIONAL INSTITUTES OF HEALTH. Report:estimates of funding for various research, condition, and disease categories (RCDC)[EB/OL].[2017-06-02]. http://report.nih.gov/categorical_spending.aspx.
[3] SONG M, KIM S, ZHANG G, DING Y, et al. Productivity and influence in bioinformatics:A bibliometric analysis using PubMed central. Journal of the association for information science and technology, 2014, 65(2), 352-371.
[4] OH Y S, GALIS Z S. Anatomy of success:the top 100 cited scientific reports focused on hypertension research[J]. Hypertension, 2014, 63(4):641-647.
[5] SCHREIBER C, EDLINGER C, EDER S, et al. Global research trends in the medical therapy of pulmonary arterial hypertension in 2000-2014[J]. Pulmonary pharmacology & therapeutics, 2016, 39(8):21-27.
[6] GOTTING M, SCHWARZER M, GERBER A, et al. Pulmonary hypertension:scientometric analysis and density-equalizing mapping[J]. Plos one, 2017, 12(1):e0169238.
[7] DING Y.Community detection:Topological vs Topical[J].Journal of informetrics, 2011, 5(4):498-514.
[8] KLAVANS R, BOYACK K W. Identifying a better measure of relatedness for mapping science[J]. Journal of the American Society for Information Science and Technology, 2006, 57(2):251-263.
[9] RONDA-PUPO G A, GUERRAS-MARTIN L A. Dynamics of the evolution of the strategy concept 1962-2008:a co-word analysis[J]. Strategic management journal, 2012, 33(2):162-188.
[10] CHEN C. CiteSpace Ⅱ:Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American society for Information Science and Technology, 2006, 57(3):359-377.
[11] SWANSON D R. Two medical literatures that are logically but not bibliographically connected[J]. Journal of the Association for Information Science. 1987:228-233.
[12] LINDSAY R K, GORDON M D. Literature-based discovery by lexical statistics[J]. Journal of the American Society for Information Science and Technology, 1999, 50(7):574-587.
[13] GORDON M D, LINDSAY R K. Toward discovery support systems:a replication, re-examination, and extension of Swanson's work on literature-based discovery of a connection between Raynaud's and fish oil[J]. Journal of the Association for Information Science and Technology, 1996, 47(2):116-128.
[14] 祝清松, 冷伏海. 基于引文内容分析的高被引论文主题识别研究[J]. 中国图书馆学报, 2014, 40(1):39-49.
[15] WASSERMAN S, FAUST K. Social network analysis:methods and applications.[J]. Contemporary sociology, 1994, 91(435):219-220.
[16] 朱庆华, 李亮. 社会网络分析法及其在情报学中的应用[J]. 情报理论与实践, 2008, 31(2):179-183.
[17] 徐媛媛, 朱庆华. 社会网络分析法在引文分析中的实证研究[J]. 情报理论与实践, 2008, 31(2):184-188.
[18] 李亮, 朱庆华. 社会网络分析方法在合著分析中的实证研究[J]. 情报科学, 2008, 26(4):549-555.
[19] BUTTS C T. Social network analysis:a methodological introduction[J]. Asian journal of social psychology, 2008, 11(1):13-41.
[20] 王洪伟, 高松, 陆頲. 基于LDA和SNA的在线新闻热点识别研究[J]. 情报学报, 2016, 35(10):1022-1037.
[21] WALLACE M L, GINGRAS Y, DUHON R.A new approach for detecting scientific specialties from raw cocitation networks[J]. Journal of the American Society for Information Science and Technology, 2009, 60(2):240-246.
[22] GROSS A, MURTHY D. Modeling virtual organizations with Latent Dirichlet Allocation:a case for natual language processing[J]. Neural networks, 2014,58:38-49.
[23] TANG X B, Fang X K. Research on the subject retrieval of the weibo based on the integration of text clustering and LDA[J]. Information studies:theory & application, 2013,8:85-90.
[24] ZHANG P J, SONG L. Overview on topic modeling method of microblogs text based on LDA[J]. Library and information service, 2012,24:120-126.
[25] DEERWESTER S, DUMAIS S T, FURNAS G W, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 2010, 41(6):391-401.
[26] HOFMANN T. Probabilistic latent semantic indexing[C]//Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. New York:ACM, 1999:50-57.
[27] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine Learning research archive, 2003, 3:993-1022.
[28] MIMNO D, MCCALLUM A. Topic models conditioned on arbitrary features with Dirichlet-multinomial Regression[J]. University of Massachusetts-Amherst, 2012, 2008:411-418.
[29] SONG M, HEO G E, LEE D. Identifying the landscape of Alzheimer's disease research with network and content analysis[J]. Scientometrics, 2015, 102(1):905-927.
[30] CUI L. Development of a text mining system based on the co-occurrence of bibliographic items in literature databases[J]. New technology of library and information service,2008, 24(8):70-75.
[31] BASTIAN M, HEYMANN S, JACOMY M. Gephi:an open source software for exploring and manipulating networks[C]//Proceedings of the third international conference on Weblogs and social media. California:ICWSM, 2009.
[32] YANG Y, WU M, CUI L. Integration of three visualization methods based on co-word analysis[J]. Scientometrics, 2012, 90(2):659-673.
[33] 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.
[34] NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks[J]. Physical review E, 2004, 69(2):026113.
[35] BLONDEL V D, GUILLAUME J L, LAMBIOTTE R,et al. Fast unfolding of communities in large networks[J]. Journal of statistical mechanics:theory and experiment, 2008(10):155-168.
[36] BRIN S, PAGE L. The anatomy of a large-scale hypertextual Web search engine[J]. Computer networks, 1998, 30(1-7):107-117.
[37] LAMBIOTTE R, DELVENNE J C, BARAHONA M. Laplacian dynamics and multiscale modular structure in networks[J]. Physics, 2008,1(2):1-29.
[38] WALLACH H M, MIMNO D M, MCCALLUM A. Rethinking LDA:Why priors matter[J]. Advances in neural information processing systems, 2009, 23:1973-1981.
[39] WANG Y, AGICHTEIN E, BENZI M. TM-LDA:efficient online modeling of latent topic transitions in social media[C]//Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. New York:ACM, 2012:123-131.
