Research on Influence Evaluation of Humanities and Social Sciences Academic Monographs from the Perspective of Altmetrics: Comparative Analysis Based on BkCI, Amazon, and Goodreads
Li Jiangbo1, Zhang Liang1, Jiang Chunlin2
1.Department of Management Science and Engineering, School of Business, Qingdao University, Qingdao 266100 2.WISE Lab, Institute of Science of Science and S&T Management, Dalian University of Technology, Dalian 116024
摘要Altmetrics自提出以来发展迅速，极大地拓宽了文献计量学评价学术成果的研究范围。目前绝大多数的Altmetrics研究集中在对论文学术成果的评价上，学术专著或其他类型学术成果的评价似乎不够受重视。然而，专著是社会科学和人文学科学术成果的重要形式，本研究将着重研究专著学术成果的评价。本文通过对出版时间标准化构建学术专著在数据库中的日均被引次数（daily average times cited，DATC）指标。此外，本文使用递归神经网络（recurrent neural network，RNN）的方法对专著的在线评论进行情感分类，并使用情感词典对评论文本进行细粒度情感分析，得到情感分析值指标。研究结果发现，部分学术图书在BkCI数据库中的被引次数较少，但其DATC指标较高，原因是这些学术专著的出版时间较晚，还没有足够的时间积累引文。这说明专著的被引次数少并不意味着它们的学术影响力一定低，仅利用引文索引数据库中的被引次数来评价学术专著的学术影响力是有缺陷的。另一个结果表明，学术专著在在线评论方面的Altmetric指标与引文相关性很小，特别是在情感分析均值指标和引文指标之间。低相关性意味着在线评论方面的Altmetric指标在评价学术专著学术影响力方面几乎没有可行性，只能用于评价学术专著的社会影响力。
李江波, 张梁, 姜春林. Altmetrics视角下的人文社会科学学术专著影响力评价研究——基于BkCI、Amazon和Goodreads的比较分析[J]. 情报学报, 2020, 39(9): 896-905.
Li Jiangbo, Zhang Liang, Jiang Chunlin. Research on Influence Evaluation of Humanities and Social Sciences Academic Monographs from the Perspective of Altmetrics: Comparative Analysis Based on BkCI, Amazon, and Goodreads. 情报学报, 2020, 39(9): 896-905.
1 Priem J, Taraborellid D, Groth P. et al. Altmetrics: A manifesto[EB/OL]. (2010-10-26) [2016-11-30]. http://altmetrics.org/mani festo. 2 邱均平, 张心源, 董克. Altmetrics指标在机构知识库中的应用研究[J]. 图书情报工作, 2015, 59(2): 100-105. 3 McFedries P. Measuring the impact of altmetrics [technically speaking][J]. IEEE Spectrum, 2012, 49(8): 28. 4 Galligan F, Dyas-Correia S. Altmetrics: Rethinking the way we measure[J]. Serials Review, 2013, 39(1): 56-61. 5 Costas R, Zahedi Z, Wouters P. Do “altmetrics” correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective[J]. Journal of the Association for Information Science and Technology, 2015, 66(10): 2003-2019. 6 Gl?nzel W, Schoepflin U. A bibliometric study of reference literature in the sciences and social sciences[J]. Information Processing & Management, 1999, 35(1): 31-44. 7 Huang M H, Chang Y W. Characteristics of research output in social sciences and humanities: From a research evaluation perspective[J]. Journal of the American Society for Information Science and Technology, 2008, 59(11): 1819-1828. 8 Nederhof A J. Bibliometric monitoring of research performance in the social sciences and the humanities: A review[J]. Scientometrics, 2006, 66(1): 81-100. 9 姜春林. 国外人文社会科学学术图书评价研究进展[J]. 西南民族大学学报(人文社科版), 2019, 40(2): 228-235. 10 Zuccala A A, Verleysen F T, Cornacchia R, et al. Altmetrics for the humanities: Comparing Goodreads reader ratings with citations to history books[J]. Aslib Journal of Information Management, 2015, 67(3): 320-336. 11 Kousha K, Thelwall M. Can Amazon.com reviews help to assess the wider impacts of books?[J]. Journal of the Association for Information Science and Technology, 2016, 67(3): 566-581. 12 何峻, 蔡蓉华. 中文图书评价体系研究[J]. 大学图书馆学报, 2016, 34(3): 51-58, 15. 13 李明, 陈铭. 学术图书Altmetrics评价指标分层框架探析[J]. 现代情报, 2018, 38(5): 106-109. 14 姜春林, 魏庆肖. 融合补充计量指标的人文社科专著影响力评价指标体系研究[J]. 情报杂志, 2018, 37(12): 65-71, 77. 15 李明, 陈铭. 中文学术图书Altmetrics评价优势、体系及难点分析[J]. 西南民族大学学报(人文社会科学版), 2019, 40(2): 236-240. 16 李明, 李江, 陈铭, 等. 中文学术图书引文量与Altmetrics指标探索性分析及其启示[J]. 情报学报, 2019, 38(6): 557-567. 17 章成志, 童甜甜, 周清清. 整合不同评论平台的图书综合影响力评价研究[J]. 情报学报, 2018, 37(9): 861-873. 18 Xiang Z, Du Q Z, Ma Y F, et al. A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism[J]. Tourism Management, 2017, 58: 51-65. 19 He R N, McAuley J. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering[C]// Proceedings of the 25th International Conference on World Wide Web. New York: ACM Press, 2016: 507-517. 20 McAuley J, Targett C, Shi Q F, et al. Image-based recommendations on styles and substitutes[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2015: 43-52. 21 Wan M T, McAuley J. Item recommendation on monotonic behavior chains[C]// Proceedings of the 12th ACM Conference on Recommender Systems. New York: ACM Press, 2018: 86-94. 22 Wan M T, Misra R, Nakashole N, et al. Fine-grained spoiler detection from large-scale review corpora[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2019: 2605-2610. 23 Abadi M, Barham P, Chen J M, et al. TensorFlow: A system for large-scale machine learning[C]// Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. Berkeley: USENIX Association, 2016: 265-283. 24 Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. 25 周清清, 章成志. 在线用户评论细粒度属性抽取[J]. 情报学报, 2017, 36(5): 484-493. 26 Zhou Q Q, Zhang C Z, Zhao S X, et al. Measuring book impact based on the multi-granularity online review mining[J]. Scientometrics, 2016, 107(3): 1435-1455. 27 Salton G, McGill M J. Introduction to modern information retrieval[M]. New York: McDraw-Hill, 1983: 30-42. 28 Smilkov D, Thorat N, Nicholson C, et al. Embedding Projector: Interactive visualization and interpretation of embeddings[OL]. https://arxiv.org/pdf/1611.05469.pdf. 29 Hu M Q, Liu B. Mining and summarizing customer reviews[C]//Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New Yok: ACM Press, 2004: 168-177. 30 Bornmann L, Haunschild R. Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data[J]. Journal of Informetrics, 2018, 12(3): 998-1011.
