|
|
|
| On the Quantification and Distribution of Citation Peaks |
| Li Lingying, Min Chao, and Sun |
| School of Information Management, Nanjing University, Nanjing 210023 |
|
|
|
|
Abstract Citations are important carriers of scientific knowledge, and a citation peak reflects the most influential stage in the process of citation diffusion. In this study, the distribution of citation peaks was analyzed to provide a better understanding of the dynamic diffusion of scientific knowledge. A citation peak identification method was applied to the citation curves of articles in publications of the American Physical Society (APS 2013). Definition and quantification were included in the analysis, and six types of articles have been summarized. In the study, peak distribution was used to distinguish citation patterns based on the number of peaks, peak position, and peak interval. This article demonstrates that the most influential stage can be explained by a simple peak model, which allows us to probe differences in peak distribution quantitatively. Citation peaks of articles showed a high degree of temporal regularity, with most articles having only one peak. The first peak and the highest peak were generally reached within a few years after publication (most within five years, especially within the first or second year). The results also show that the first peak position has a positive and significant correlation with the distribution of the highest peak. We also found that highly cited papers are more likely to reach the first peak in the early phase of publication.
|
|
Received: 04 December 2018
|
|
|
|
1 GarfieldE. Citation analysis as a tool in journal evaluation[J]. Science, 1972, 178(4060): 471-479.
2 MohlerB A. Citation analysis as an assessment tool[J]. Science & Technology Libraries, 2005, 25(4): 57-64.
3 MoedH F. Citation analysis in research evaluation[J]. Information Science and Knowledge Management, 2011, 57(1): 13-18.
4 GarfieldE. The history and meaning of the journal impact factor[J]. Journal of the American Medical Association, 2006, 295(1): 90-93.
5 CroninB, MehoL. Using the h-index to rank influential information scientists[J]. Journal of the American Society for Information Science and Technology, 2006, 57(9): 1275-1278.
6 郭倩影, 杜建, 唐小利. 学术传承意义上“学术链”的识别方法探讨——以2014年诺贝尔化学奖为例[J]. 情报资料工作, 2018(2): 29-36.
7 MinC, DingY, LiJ, et al. Innovation or imitation: The diffusion of citations[J]. Journal of the Association for Information Science and Technology, 2018, 69(10): 1271-1282.
8 CanoV, LindN C. Citation life cycles of ten citation classics[J]. Scientometrics, 1991, 22(2): 297-312.
9 BaumgartnerS E, LeydesdorffL. Group-based trajectory modeling (GBTM) of citations in scholarly literature: Dynamic qualities of “transient” and “sticky knowledge claims”[J]. Journal of the Association for Information Science and Technology, 2014, 65(4): 797-811.
10 LiJ, YeF Y. A probe into the citation patterns of high-quality and high-impact publications[J]. Malaysian Journal of Library and Information Science, 2014, 19(2): 17-33.
11 van RaanA F J. Sleeping beauties in science[J]. Scientometrics, 2004, 59(3): 467-472.
12 KeQ, FerraraE, RadicchiF, et al. Defining and identifying Sleeping Beauties in science[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(24): 7426-7431.
13 LiJ, ShiD B, ZhaoS X, et al. A study of the “heartbeat spectra” for “sleeping beauties”[J]. Journal of Informetrics, 2014, 8(3): 493-502.
14 LiJ, YeF Y. Distinguishing sleeping beauties in science[J]. Scientometrics, 2016, 108(2): 821-828.
15 闵超, YingDing, 李江, 等. 单篇论著的引文扩散[J]. 情报学报, 2018, 37(4): 341-350.
16 ColavizzaG, FranceschetM. Clustering citation histories in the Physical Review[J]. Journal of Informetrics, 2016, 10(4): 1037-1051.
17 BornmannL, YeA, YeF. Identifying landmark publications in the long run using field-normalized citation data[J]. Journal of Documentation, 2018, 74(2): 278-288.
18 HeidornP B. Shedding light on the dark data in the long tail of science[J]. Library Trends, 2008, 57(2): 280-299.
19 PeruzzoF. Sleeping beauties and the citation dynamics in the network of scientific papers[EB/OL]. [2018-11-16]. http://tesi.cab.unipd.it/50039/1/Peruzzo_Fabio.pdf.
20 杜建, 武夷山. 一个用于识别睡美人文献的新的无参数指标——基于“Science”和“Nature”上睡美人文献的验证[J]. 情报理论与实践, 2017, 40(2): 19-25.
21 SunJ J, MinC, LiJ. A vector for measuring obsolescence of scientific articles[J]. Scientometrics, 2016, 107(2): 745-757.
22 YeF Y, BornmannL. “Smart girls” versus “sleeping beauties” in the sciences: The identification of instant and delayed recognition by using the citation angle[J]. Journal of the Association for Information Science and Technology, 2018, 69(3): 359-367.
23 EggheL, BornmannL, GunsR. A proposal for a first-citation-speed-index[J]. Journal of Informetrics, 2011, 5(1): 181-186.
24 RiniaE J, Van LeeuwenT N, BruinsE E W, et al. Citation delay in interdisciplinary knowledge exchange[J]. Scientometrics, 2001, 51(1): 293-309.
25 Gl?nzelW, RousseauR, ZhangL. A visual representation of relative first-citation times[J]. Journal of the American Society for Information Science and Technology, 2012, 63(7): 1420-1425.
26 ChakrabortyT, KumarS, GoyalP, et al. Towards a stratified learning approach to predict future citation counts[C]//Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries. Piscataway: IEEE Press, 2014: 351-360.
27 GarnerJ, PorterA L, NewmanN C. Distance and velocity measures: using citations to determine breadth and speed of research impact[J]. Scientometrics, 2014, 100(3): 687-703.
28 Della SalaS, GrafmanJ. Five-year impact factor[J]. Cortex, 2009, 45(8): 911.
29 LiuX L, GaiS S, ZhangS L, et al. An analysis of peer-reviewed scores and impact factors with different citation time windows: A case study of 28 ophthalmologic journals[J]. PLoS ONE, 2015, 10(8): e135583.
30 赵星. JCR五年期影响因子探析[J]. 中国图书馆学报, 2010, 36(3): 120-126.
31 李江, 姜明利, 李玥婷. 引文曲线的分析框架研究——以诺贝尔奖得主的引文曲线为例[J]. 中国图书馆学报, 2014, 40(2): 41-49.
32 LiJ. Citation curves of “all-elements-sleeping-beauties”: “flash in the pan” first and then “delayed recognition”[J]. Scientometrics, 2014, 100(2): 595-601.
33 BouabidH. Revisiting citation aging: a model for citation distribution and life-cycle prediction[J]. Scientometrics, 2011, 88(1): 199-211.
34 王海燕, 马峥, 潘云涛, 等. 高被引论文与“睡美人”论文引用曲线及影响因素研究[J]. 图书情报工作, 2015, 59(16): 83-89.
35 PichappanP, PonnuduraiR. Skewness in citation peak[C]// Proceedings of the 7th Conference of the International Society for Scientometrics and Informetrics. Colima: University of Colima, 1999.
36 SinatraR, WangD S, DevilleP, et al. Quantifying the evolution of individual scientific impact[J]. Science, 2016, 354(6312): aaf5239.
37 ZhaoS X, LiJ. Citation peaks in modern science: 1900-2010[J]. Current Science, 2015, 109(9): 1523-1525.
38 PonomarevI V, WilliamsD E, HackettC J, et al. Predicting highly cited papers: A method for early detection of candidate breakthroughs[J]. Technological Forecasting and Social Change, 2014, 81: 49-55.
39 LachanceC, LariviereV. On the citation lifecycle of papers with delayed recognition[J]. Journal of Informetrics, 2014, 8(4): 863-872.
40 YoonS J, YoonD Y, LeeH J, et al. Distribution of citations received by scientific papers published in the imaging literature from 2001 to 2010: Decreasing inequality and polarization[J]. American Journal of Roentgenology, 2017, 209(2): 248-254.
41 MinC, SunJ J, PeiL, et al. Measuring delayed recognition for papers: Uneven weighted summation and total citations[J]. Journal of Informetrics, 2016, 10(4): 1153-1165.
42 LiuL, WangY, SinatraR, et al. Hot streaks in artistic, cultural, and scientific careers[J]. Nature, 2018, 559(7714): 396-399.
43 De Solla PriceD. A general theory of bibliometric and other cumulative advantage processes[J]. Journal of the American Society for Information Science, 1976, 27(5): 292-306.
44 LariviereV, GingrasY. The impact factor’s Matthew effect: A natural experiment in bibliometrics[J]. Journal of the Association for Information Science and Technology, 2010, 61(2): 424-427.
45 WangD S, SongC M, BarabasiA. Quantifying long-term scientific impact[J]. Science, 2013, 342(6154): 127-132.
46 BornmannL, MutzR. Further steps towards an ideal method of measuring citation performance: The avoidance of citation (ratio) averages in field-normalization[J]. Journal of Informetrics, 2011, 5(1): 228-230.
47 BianconiG, BarabásiA L. Bose-Einstein condensation in complex networks[J]. Physical Review Letters, 2001, 86(24): 5632-5635.
48 BarabásiA. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512.
49 NashJ R, AraújoR J, ShidelerG S. Contributing factors to long-term citation count in marine and freshwater biology articles[J]. Learned Publishing, 2018, 31(2): 131-139.
50 StegehuisC, LitvakN, WaltmanL. Predicting the long-term citation impact of recent publications[J]. Journal of Informetrics, 2015, 9(3): 642-657.
51 SternD I. High-ranked social science journal articles can be identified from early citation information[J]. PLoS ONE, 2014, 9(11): e112520.
52 李正风, 梁永霞. 引文动机的生态学解释[J]. 科学学研究, 2012, 30(4): 487-494.
53 赵蓉英, 曾宪琴, 陈必坤. 全文本引文分析——引文分析的新发展[J]. 图书情报工作, 2014, 58(9): 129-135. |
|
|
|
