@article{albarrn2011references, abstract = {This article studies massive evidence about references made and citations received after a 5-year citation window by 3.7 million articles published in 1998 to 2002 in 22 scientific fields. We find that the distributions of references made and citations received share a number of basic features across sciences. Reference distributions are rather skewed to the right while citation distributions are even more highly skewed: The mean is about 20 percentage points to the right of the median, and articles with a remarkable or an outstanding number of citations represent about 9% of the total. Moreover, the existence of a power law representing the upper tail of citation distributions cannot be rejected in 17 fields whose articles represent 74.7% of the total. Contrary to the evidence in other contexts, the value of the scale parameter is above 3.5 in 13 of the 17 cases. Finally, power laws are typically small, but capture a considerable proportion of the total citations received.}, author = {Albarrán, Pedro and Ruiz-Castillo, Javier}, doi = {10.1002/asi.21448}, interhash = {79502663727fcbd4834a423f4e3212a3}, intrahash = {f20e50e960696bab3b39b628718dd850}, issn = {1532-2890}, journal = {Journal of the American Society for Information Science and Technology}, number = 1, pages = {40--49}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, title = {References made and citations received by scientific articles}, url = {http://dx.doi.org/10.1002/asi.21448}, volume = 62, year = 2011 } @article{cerinek2015network, abstract = {We analyze the data about works (papers, books) from the time period 1990–2010 that are collected in Zentralblatt MATH database. The data were converted into four 2-mode networks (works }, author = {Cerinšek, Monika and Batagelj, Vladimir}, doi = {10.1007/s11192-014-1419-z}, interhash = {e65f748684210857bb19dc7f69d65f86}, intrahash = {bcba93fd0e6381289c489cbab20bbec7}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 1, pages = {977-1001}, publisher = {Springer Netherlands}, title = {Network analysis of Zentralblatt MATH data}, url = {http://dx.doi.org/10.1007/s11192-014-1419-z}, volume = 102, year = 2015 } @article{bonzi1991motivations, abstract = {The citation motivations among 51 self citing authors in several natural science disciplines were investigated. Results of a survey on reasons for both self citation and citation to others show that there are very few differences in motivation, and that there are plausible intellectual grounds for those differences which are substantial. Analysis of exposure in text reveals virtually no differences between self citations and citations to others. Analysis of individual disciplines also uncover no substantive differences in either motivation or exposure in text.}, author = {Bonzi, Susan and Snyder, H.W.}, doi = {10.1007/BF02017571}, interhash = {b531a253fae4751735918d6d5c8b44bd}, intrahash = {fcd88cce5ca6a7c99cb4726921752a1b}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 2, pages = {245-254}, publisher = {Kluwer Academic Publishers}, title = {Motivations for citation: A comparison of self citation and citation to others}, url = {http://dx.doi.org/10.1007/BF02017571}, volume = 21, year = 1991 } @article{phelan1999compendium, abstract = {This paper examines a number of the criticisms that citation analysis has been subjected to over the years. It is argued that many of these criticisms have been based on only limited examinations of data in particular contexts and it remains unclear how broadly applicable these problems are to research conducted at different levels of analysis, in specific field, and among various national data sets. Relevant evidence is provided from analysis of Australian and international data. }, author = {Phelan, Thomas J.}, doi = {10.1007/BF02458472}, interhash = {a8e468c0850ef735517484b121e30630}, intrahash = {a9d0ef4078c380cb07619a545ed4144d}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 1, pages = {117-136}, publisher = {Kluwer Academic Publishers}, title = {A compendium of issues for citation analysis}, url = {http://dx.doi.org/10.1007/BF02458472}, volume = 45, year = 1999 } @article{bornmann2008citation, abstract = {Purpose – The purpose of this paper is to present a narrative review of studies on the citing behavior of scientists, covering mainly research published in the last 15 years. Based on the results of these studies, the paper seeks to answer the question of the extent to which scientists are motivated to cite a publication not only to acknowledge intellectual and cognitive influences of scientific peers, but also for other, possibly non‐scientific, reasons.Design/methodology/approach – The review covers research published from the early 1960s up to mid‐2005 (approximately 30 studies on citing behavior‐reporting results in about 40 publications).Findings – The general tendency of the results of the empirical studies makes it clear that citing behavior is not motivated solely by the wish to acknowledge intellectual and cognitive influences of colleague scientists, since the individual studies reveal also other, in part non‐scientific, factors that play a part in the decision to cite. However, the results of the studies must also be deemed scarcely reliable: the studies vary widely in design, and their results can hardly be replicated. Many of the studies have methodological weaknesses. Furthermore, there is evidence that the different motivations of citers are “not so different or ‘randomly given’ to such an extent that the phenomenon of citation would lose its role as a reliable measure of impact”.Originality/value – Given the increasing importance of evaluative bibliometrics in the world of scholarship, the question “What do citation counts measure?” is a particularly relevant and topical issue. }, author = {Bornmann, Lutz and Daniel, Hans‐Dieter}, doi = {10.1108/00220410810844150}, eprint = {http://dx.doi.org/10.1108/00220410810844150}, interhash = {ef016be783f4956817cded258543ece3}, intrahash = {544d3243f7c7327b946292a80f9b6451}, journal = {Journal of Documentation}, number = 1, pages = {45-80}, title = {What do citation counts measure? A review of studies on citing behavior}, url = {http://dx.doi.org/10.1108/00220410810844150 }, volume = 64, year = 2008 } @inproceedings{saeed2008citation, abstract = {New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.}, author = {Saeed, A.U. and Afzal, M.T. and Latif, A. and Tochtermann, K.}, booktitle = {Multitopic Conference, 2008. INMIC 2008. IEEE International}, doi = {10.1109/INMIC.2008.4777769}, interhash = {26d1785cab132d577e377bb5bf299002}, intrahash = {677fc89fef6c79a6a4f25cb25246e38a}, month = dec, pages = {392-397}, title = {Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papers}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4777769}, year = 2008 } @article{thijs2006influence, abstract = {In earlier studies by the authors, basic regularities of author self-citations have been analysed. These regularities are related to the ageing, to the relation between self-citations and foreign citations, to the interdependence of self-citations with other bibliometric indicators and to the influence of co-authorship on self-citation behaviour. Although both national and subject specific peculiarities influence the share of self-citations at the macro level, the authors came to the conclusion that - at this level of aggregation - there is practically no need for excluding self-citations. The aim of the present study is to answer the question in how far the influence of author self-citations on bibliometric meso-indicators deviates from that at the macro level, and to what extent national reference standards can be used in bibliometric meso analyses. In order to study the situation at the institutional level, a selection of twelve European universities representing different countries and different research profiles have been made. The results show a quite complex situation at the meso-level, therefore we suggest the usage of both indicators, including and excluding self-citations.}, affiliation = {Katholieke Universiteit Leuven, Steunpunt O&O Statistieken Leuven (Belgium) Leuven (Belgium)}, author = {Thijs, Bart and Glänzel, Wolfgang}, interhash = {82ea078d91ba87557fb69d7fba5171bc}, intrahash = {c360454b0f49b781ccbbe16840f54b35}, issn = {0138-9130}, journal = {Scientometrics}, keyword = {Informatik}, note = {10.1007/s11192-006-0006-3}, number = 1, pages = {71-80}, publisher = {Akadémiai Kiadó, co-published with Springer Science+Business Media B.V., Formerly Kluwer Academic Publishers B.V.}, title = {The influence of author self-citations on bibliometric meso-indicators. The case of european universities}, url = {http://dx.doi.org/10.1007/s11192-006-0006-3}, volume = 66, year = 2006 } @article{haustein2011applying, abstract = {Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in STM and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content.}, author = {Haustein, Stefanie and Siebenlist, Tobias}, doi = {10.1016/j.joi.2011.04.002}, interhash = {13fe59aae3d6ef95b529ffe00ede4126}, intrahash = {60170943fb293bcb54754710ec9dced1}, issn = {1751-1577}, journal = {Journal of Informetrics}, number = 3, pages = {446 - 457}, title = {Applying social bookmarking data to evaluate journal usage}, url = {http://www.sciencedirect.com/science/article/pii/S1751157711000393}, volume = 5, year = 2011 } @article{fu2008models, abstract = {The single most important bibliometric criterion for judging the impact of biomedical papers and their authors work is the number of citations received which is commonly referred to as citation count. This metric however is unavailable until several years after publication time. In the present work, we build computer models that accurately predict citation counts of biomedical publications within a deep horizon of ten years using only predictive information available at publication time. Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and their statistical analysis provides greater insight into citation behavior.}, author = {Fu, Lawrence D. and Aliferis, Constantin}, interhash = {1eb972fa9ba9e255d6889b01532ea767}, intrahash = {39d155a532108bc71437451e31287943}, journal = {AMIA Annu Symp Proc}, pages = {222-226}, pmid = {18999029}, title = {Models for predicting and explaining citation count of biomedical articles}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2656101/}, year = 2008 } @article{lokker2008prediction, author = {Lokker, Cynthia and McKibbon, K Ann and McKinlay, R James and Wilczynski, Nancy L and Haynes, R Brian}, doi = {10.1136/bmj.39482.526713.BE}, interhash = {f5f066ee09051d862c1a1c9f34a832c0}, intrahash = {dece3577294846d48f198a6a5e6425c2}, journal = {BMJ}, month = {3}, number = 7645, pages = {655--657}, title = {Prediction of citation counts for clinical articles at two years using data available within three weeks of publication: retrospective cohort study}, volume = 336, year = 2008 } @article{hirsch2007index, abstract = {Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h index compared with other indicators. Our findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h index.}, author = {Hirsch, J. E.}, doi = {10.1073/pnas.0707962104}, eprint = {http://www.pnas.org/content/104/49/19193.full.pdf+html}, interhash = {9bc6518ef60bb256ca78287a6c349f05}, intrahash = {43caaad4f117fc3f5c14d83b9082448e}, journal = {Proceedings of the National Academy of Sciences}, number = 49, pages = {19193-19198}, title = {Does the h index have predictive power?}, url = {http://www.pnas.org/content/104/49/19193.abstract}, volume = 104, year = 2007 } @inproceedings{yan2011citation, abstract = {In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to track all existed publications because the volume of literature collection grows extremely fast. Therefore, researchers generally follow, or cite merely a small proportion of publications which they are interested in. For such a large collection, it is rather interesting to forecast which kind of literature is more likely to attract scientists' response. In this paper, we use the citations as a measurement for the popularity among researchers and study the interesting problem of Citation Count Prediction (CCP) to examine the characteristics for popularity. Estimation of possible popularity is of great significance and is quite challenging. We have utilized several features of fundamental characteristics for those papers that are highly cited and have predicted the popularity degree of each literature in the future. We have implemented a system which takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R-square). Experimental results on a real-large data set show that the best predictive model achieves a mean average predictive performance of 0.740 measured in R-square, which significantly outperforms several alternative algorithms.}, acmid = {2063757}, address = {New York, NY, USA}, author = {Yan, Rui and Tang, Jie and Liu, Xiaobing and Shan, Dongdong and Li, Xiaoming}, booktitle = {Proceedings of the 20th ACM international conference on Information and knowledge management}, doi = {10.1145/2063576.2063757}, interhash = {71ec0933a36df3dd21f38285bdf9b1b0}, intrahash = {b0caabb6e17d9b790d3f13c897330aad}, isbn = {978-1-4503-0717-8}, location = {Glasgow, Scotland, UK}, numpages = {6}, pages = {1247--1252}, publisher = {ACM}, series = {CIKM '11}, title = {Citation count prediction: learning to estimate future citations for literature}, url = {http://doi.acm.org/10.1145/2063576.2063757}, year = 2011 }