Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (11): 75-81    DOI: 10.11925/infotech.1003-3513.2015.11.11
Current Issue | Archive | Adv Search |
An Analysis of the Accumulation State and the Validity of User Readership Data in Online Reference Managers ——Take the Indicators of Altmetrics as an Example
Jin Wei, Zhao Rongying, Yin Ge
School of Information Management, Wuhan University, Wuhan 430072, China
Export: BibTeX | EndNote (RIS)      

[Objective] The research investigates whether user readership data in Mendeley is reliable and useful in evaluating scientific literatures and whether user readership data can reveal high quality articles, to validate the indicators of Altmetrics in scientific evaluation.[Methods] The paper selects a number of articles, collects these articles' citations in Web of Science (WoS) and Google Scholar (GS) and user readership data in Mendeley, and then makes statistical and correlational analyses.[Results] Mendeley has accumulated much more user data than before. Articles' user readership data have strong relationship with the citations in WoS and GS. However, the relationship between user counts and citations in the articles that have highest citations in WoS is relatively weaker.[Limitations] In this research, articles come from less journals in a specific field, that may make it be lack of representativeness and comprehensiveness.[Conclusions] User readership data could be useful to act as a supplement of present scientific evaluation indicators.

Received: 28 May 2015      Published: 06 April 2016
:  G250  

Cite this article:

Jin Wei, Zhao Rongying, Yin Ge. An Analysis of the Accumulation State and the Validity of User Readership Data in Online Reference Managers ——Take the Indicators of Altmetrics as an Example. New Technology of Library and Information Service, 2015, 31(11): 75-81.

URL:     OR

[1] Garfield E. Citation Indexing for Studying Science [J]. Nature, 1970, 227(5259): 669-671.
[2] Garfield E. Citation Analysis as a Tool in Journal Evaluation [J]. Science, 1972, 178(4060): 471-479.
[3] McSweeney P, Prince R, Hargood C, et al. Aggregated Erevnametrics: Bringing Together Alt-metrics Through Research Objects [C]. In: Proceedings of the 2011 ACM Web Science Conference, Koblenz, Germany. 2011.
[4] MacRoberts M H, MacRoberts B R. Problems of Citation Analysis: A Study of Uncited and Seldom-Cited Influences [J]. Journal of the American Society for Information Science and Technology, 2010, 61(1): 1-12.
[5] MacRoberts M H, MacRoberts B R. Problems of Citation Analysis [J]. Scientometrics, 1996, 36(3): 435-444.
[6] Alhoori H, Furuta R. Can Social Reference Management Systems Predict a Ranking of Scholarly Venues [A]. // Research and Advanced Technology for Digital Libraries [M]. Springer Berlin Heidelberg, 2013.
[7] Thelwall M. Journal Impact Evaluation: A Webometric Perspective [J]. Scientometrics, 2012, 92(2): 429-441.
[8] Brody T, Harnad S, Carr L. Earlier Web Usage Statistics as Predictors of Later Citation Impact [J]. Journal of the American Society for Information Science and Technology, 2006, 57(8): 1060-1072.
[9] Sutherland W J, Goulson D, Potts S G, et al. Quantifying the Impact and Relevance of Scientific Research [J]. PLoS One, 2011, 6(11): e27537.
[10] Tarrant D, Carr L. Using the Co-Citation Network to Indicate Article Impact [C]. In: Proceedings of the 2011 ACM Web Science Conference, Koblenz, Germany. 2011.
[11] González-Pereira B, Vicente G B, Moya-Anegón F. A New Approach to the Metric of Journals' Scientific Prestige: The SJR Indicator [J]. Journal of Informetrics, 2010, 4(3): 379-391.
[12] Torres-Salinas D, Cabezas-Clavijo Á, Jiménez-Contreras E. Altmetrics: New Indicators for Scientific Communication in Web 2.0 [OL]. arXiv:1306.6595.
[13] Bornmann L. Do Altmetrics Point to the Broader Impact of Research? An Overview of Benefits and Disadvantages of Altmetrics [J]. Journal of Informetrics, 2014, 8(4): 895-903.
[14] Roemer R C, Borchardt R. From Bibliometrics to Altmetrics: A Changing Scholarly Landscape [J]. College & Research Libraries News, 2012, 73(10): 596-600.
[15] De Winter J C F. The Relationship Between Tweets, Citations, and Article Views for PLoS One Articles [J]. Scientometrics, 2014, 102(2): 1773-1779.
[16] Thelwall M, Haustein S, Larivière V, et al. Do Altmetrics Work? Twitter and Ten Other Social Web Services [J]. PLoS One, 2013, 8(5): e64841.
[17] Haustein S, Peters I, Bar-Ilan J, et al. Coverage and Adoption of Altmetrics Sources in the Bibliometric Community [J]. Scientometrics, 2014, 101(2): 1145-1163.
[18] Zahedi Z, Costas R, Wouters P. How Well Developed are Altmetrics? A Cross-Disciplinary Analysis of the Presence of ‘Alternative Metrics' in Scientific Publications [J]. Scientometrics, 2014,101(2): 1491-1513.
[19] Li X, Thelwall M, Giustini D. Validating Online Reference Managers for Scholarly Impact Measurement [J]. Scientometrics, 2012, 91(2): 461-471.
[20] Sud P, Thelwall M. Evaluating Altmetrics [J]. Scientometrics, 2014, 98(2): 1131-1143.
[21] 王睿, 胡文静, 郭玮. 高Altmetrics指标科技论文学术影响力研究[J]. 图书情报工作, 2014,58(21): 92-98.(Wang Rui, Hu Wenjing, Guo Wei. Research on Academic Influence of High Altmetrics Sci-tech Papers[J]. Library and Information Service, 2014, 58(21): 92-98. )
[22] 由庆斌, 韦博, 汤珊红. 基于补充计量学的论文影响力评价模型构建[J]. 图书情报工作, 2014, 58(22): 5-11. (You Qingbin, Wei Bo, Tang Shanhong. Evaluation Model Construction to Evaluate Article's Influence Based on Altmetrics [J]. Library and Information Service, 2014, 58(22): 5-11.)
[23] 刘春丽, 何钦成. 不同类型选择性计量指标评价论文相关性研究——基于 Mendeley、F1000和Google Scholar三种学术社交网络工具[J]. 情报学报, 2013, 32(2): 206-212. (Liu Chunli, He Qincheng. Study on Correlation of Different Altmetrics Indicators for Paper Evaluation Based on Three Academic Social Networking Tools: Mendeley, F1000 and Google Scholar [J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(2): 206-212.)
[24] Mohammadi E, Thelwall M. Mendeley Readership Altmetrics for the Social Sciences and Humanities: Research Evaluation and Knowledge Flows [J]. Journal of the Association for Information Science and Technology, 2014, 65(8): 1627-1638.
[25] Mas-Bleda A, Thelwall M, Kousha K, et al. Do Highly Cited Researchers Successfully Use the Social Web? [J]. Sciento­metrics, 2014, 101(1): 337-356.
[26] Moed H F. Citation Analysis in Research Evaluation [M]. Springer Netherlands, 2006.

[1] Chai Qingfeng, Shi Linyan, Mei Shan, Xiong Haitao, He Huixin. Extracting Knowledge Elements of Sci-Tech Literature Based on Artificial and Machine Features[J]. 数据分析与知识发现, 2021, 5(8): 132-144.
[2] Tan Ying, Tang Yifei. Extracting Citation Contents with Coreference Resolution[J]. 数据分析与知识发现, 2021, 5(8): 25-33.
[3] Wang Qinjie, Qin Chunxiu, Ma Xubu, Liu Huailiang, Xu Cunzhen. Recommending Scientific Literature Based on Author Preference and Heterogeneous Information Network[J]. 数据分析与知识发现, 2021, 5(8): 54-64.
[4] Han Pu,Zhang Zhanpeng,Zhang Mingtao,Gu Liang. Normalizing Chinese Disease Names with Multi-feature Fusion[J]. 数据分析与知识发现, 2021, 5(5): 83-94.
[5] Li He,Liu Jiayu,Li Shiyu,Wu Di,Jin Shuaiqi. Optimizing Automatic Question Answering System Based on Disease Knowledge Graph[J]. 数据分析与知识发现, 2021, 5(5): 115-126.
[6] Li Yueyan,Wang Hao,Deng Sanhong,Wang Wei. Research Trends of Information Retrieval——Case Study of SIGIR Conference Papers[J]. 数据分析与知识发现, 2021, 5(4): 13-24.
[7] Yi Huifang,Liu Xiwen. Analyzing Patent Technology Topics with IPC Context-Enhanced Context-LDA Model[J]. 数据分析与知识发现, 2021, 5(4): 25-36.
[8] Wang Hongbin,Wang Jianxiong,Zhang Yafei,Yang Heng. Topic Recognition of News Reports with Imbalanced Contents[J]. 数据分析与知识发现, 2021, 5(3): 109-120.
[9] Chang Zhijun,Qian Li,Xie Jing,Wu Zhenxin,Zhang Hu,Yu Qianqian,Wang Ying,Wang Yongji. Big Data Platform for Sci-Tech Literature Based on Distributed Technology[J]. 数据分析与知识发现, 2021, 5(3): 69-77.
[10] Hu Shaohu,Zhang Yingyi,Zhang Chengzhi. Review of Keyword Extraction Studies[J]. 数据分析与知识发现, 2021, 5(3): 45-59.
[11] Liu Tong, Liu Chen, Ni Weijian. A semi-supervised Chinese sentiment analysis method based on multi-level data augmentation [J]. 数据分析与知识发现, 0, (): 1-.
[12] Wang Hongbin, Wang Jianxiong, Zhang Yafei, Yang Heng. Topic Recognition Research on Topic Imbalanced News Text Data Set [J]. 数据分析与知识发现, 0, (): 1-.
[13] Sifan Zhang, Zhendong Niu, Hao Lu, Yifan Zhu, Rongrong Wang. Graph Convolution Embedding and Feature Cross Based Literature Citation Prediction Method:Taking the Transportation Field as An Example [J]. 数据分析与知识发现, 0, (): 1-.
[14] Qi Ruihua, Jian Yue, Guo Xu, Guan Jinghua, Yang Mingxi. Sentiment Analysis of Cross-Domain Product Reviews Based on Feature Fusion and Attention Mechanism [J]. 数据分析与知识发现, 0, (): 1-.
[15] Li Jiao, Huang Yongwen, Luo Tingting, Zhao Ruixue, Xian Guojian. Automatic Classification based on Multi-factor Algorithm [J]. 数据分析与知识发现, 0, (): 1-.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938