@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 } @article{bjrneborn2004toward, abstract = {In this article, we define webometrics within the framework of informetric studies and bibliometrics, as belonging to library and information science, and as associated with cybermetrics as a generic subfield. We develop a consistent and detailed link typology and terminology and make explicit the distinction among different Web node levels when using the proposed conceptual framework. As a consequence, we propose a novel diagram notation to fully appreciate and investigate link structures between Web nodes in webometric analyses. We warn against taking the analogy between citation analyses and link analyses too far.}, author = {Björneborn, Lennart and Ingwersen, Peter}, doi = {10.1002/asi.20077}, interhash = {1c12570779b33a5a4a0ea079bd7b26a9}, intrahash = {1ca6a8f4b30106cac0967307f412910f}, issn = {1532-2890}, journal = {Journal of the American Society for Information Science and Technology}, number = 14, pages = {1216--1227}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, title = {Toward a basic framework for webometrics}, url = {http://dx.doi.org/10.1002/asi.20077}, volume = 55, year = 2004 } @inproceedings{yan2012better, abstract = {Usually scientists breed research ideas inspired by previous publications, but they are unlikely to follow all publications in the unbounded literature collection. The volume of literature keeps on expanding extremely fast, whilst not all papers contribute equal impact to the academic society. Being aware of potentially influential literature would put one in an advanced position in choosing important research references. Hence, estimation of potential influence is of great significance. We study a challenging problem of identifying potentially influential literature. We examine a set of hypotheses on what are the fundamental characteristics for highly cited papers and find some interesting patterns. Based on these observations, we learn to identify potentially influential literature via Future Influence Prediction (FIP), which aims to estimate the future influence of literature. The system 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 (R2). Experimental results on a real-large data set show a mean average predictive performance of 83.6% measured in R^2. We apply the learned model to the application of bibliography recommendation and obtain prominent performance improvement in terms of Mean Average Precision (MAP).}, acmid = {2232831}, address = {New York, NY, USA}, author = {Yan, Rui and Huang, Congrui and Tang, Jie and Zhang, Yan and Li, Xiaoming}, booktitle = {Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries}, doi = {10.1145/2232817.2232831}, interhash = {85d10c6d37bcbfa057c51acc325a8116}, intrahash = {9269d2dd9bf4bc8c0e7c668011fcfc1b}, isbn = {978-1-4503-1154-0}, location = {Washington, DC, USA}, numpages = {10}, pages = {51--60}, publisher = {ACM}, series = {JCDL '12}, title = {To better stand on the shoulder of giants}, url = {http://doi.acm.org/10.1145/2232817.2232831}, year = 2012 } @article{fu2014academic, abstract = {By means of their academic publications, authors form a social network. Instead of sharing casual thoughts and photos (as in Facebook), authors select co-authors and reference papers written by other authors. Thanks to various efforts (such as Microsoft Academic Search and DBLP), the data necessary for analyzing the academic social network is becoming more available on the Internet. What type of information and queries would be useful for users to discover, beyond the search queries already available from services such as Google Scholar? In this paper, we explore this question by defining a variety of ranking metrics on different entities—authors, publication venues, and institutions. We go beyond traditional metrics such as paper counts, citations, and h-index. Specifically, we define metrics such as }, author = {Fu, TomZ.J. and Song, Qianqian and Chiu, DahMing}, doi = {10.1007/s11192-014-1356-x}, interhash = {a39d784173e693ac65979737e96c2a3c}, intrahash = {de2f3434421912af52e355578e147b0a}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 1, pages = {203-239}, publisher = {Springer Netherlands}, title = {The academic social network}, url = {http://dx.doi.org/10.1007/s11192-014-1356-x}, volume = 101, year = 2014 } @article{tagliacozzo1977selfcitations, abstract = { The purpose of the present investigation was to determine to what extent authors of scientific articles cite their previous publications and what are the principal distinguishing features of this particular type of citation. We have analysed the bibliographies of a group of articles from the areas of plant physiology and neurobiology, and have examined the relationship of the self‐citations to some characteristics of the articles in which they occurred. We found self‐citations to be more recent and to be cited more frequently in the text of the citing articles than citations of other authors. The extent of self‐citing did not appear to be related to the number of co‐authors and to the bibliography size of the citing articles, or to their authors' productivity. }, author = {Tagliacozzo, Renata}, doi = {10.1108/eb026644}, eprint = {http://dx.doi.org/10.1108/eb026644}, interhash = {9a8aadb919602dd99f6d908dca28c79d}, intrahash = {f27546fa3614d1c6e202d226cb5b1b5e}, journal = {Journal of Documentation}, number = 4, pages = {251-265}, title = {Self‐Citations in Scientific Literature}, url = {http://dx.doi.org/10.1108/eb026644 }, volume = 33, year = 1977 } @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 } @inproceedings{marco2004hedging, abstract = {Citations in scientific writing fulfil an important role in creating relationships among mutually relevant articles within a research field. These inter-article relationships reinforce the argumentation structure intrinsic to all scientific writing. Therefore, determining the nature of the exact relationship between a citing and cited paper requires an understanding of the rhetorical relations within the argumentative context in which a citation is placed. To automatically determine these relations, we have suggested that various stylistic and rhetorical cues will be significant. One such cue that we are studying is the use of hedging to modify the affect of a scientific claim. We have previously shown that hedging occurs more frequently in citation contexts than in the text as a whole. With this information we conjecture that hedging is a significant aspect of the rhetorical structure of citation contexts and that the pragmatics of hedges may help in determining the rhetorical purpose of citations.}, author = {Marco, Chrysanne Di and Mercer, Robert E.}, booktitle = {Proc. AAAI Spring Symposium}, interhash = {7c4532214d65b2ef202fd2a7d7311901}, intrahash = {90a6c053558d98c9bfdbb6b4d6d8d725}, organization = {AAAI}, title = {Hedging in Scientific Articles as a Means of Classifying Citations}, url = {http://www.aaai.org/Library/Symposia/Spring/2004/ss04-07-009.php}, year = 2004 } @article{liu2012fulltext, author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun}, interhash = {011df26355ad51a88947017fd2791a98}, intrahash = {f9c6133bf4503003822f99860f864698}, journal = {Journal of the American Society for Information Science and Technology}, title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network}, url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf}, year = 2012 } @article{liu2012fulltext, author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun}, interhash = {011df26355ad51a88947017fd2791a98}, intrahash = {f9c6133bf4503003822f99860f864698}, journal = {Journal of the American Society for Information Science and Technology}, title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network}, url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf}, year = 2012 } @article{masbleda2014highly, abstract = {Academics can now use the web and the social websites to disseminate scholarly information in a variety of different ways. Although some scholars have taken advantage of these new online opportunities, it is not clear how widespread their uptake is or how much impact they can have. This study assesses the extent to which successful scientists have social web presences, focusing on one influential group: highly cited researchers working at European institutions. It also assesses the impact of these presences. We manually and systematically identified if the European highly cited researchers had profiles in Google Scholar, Microsoft Academic Search, Mendeley, Academia and LinkedIn or any content in SlideShare. We then used URL mentions and altmetric indicators to assess the impact of the web presences found. Although most of the scientists had an institutional website of some kind, few had created a profile in any social website investigated, and LinkedIn—the only non-academic site in the list—was the most popular. Scientists having one kind of social web profile were more likely to have another in many cases, especially in the life sciences and engineering. In most cases it was possible to estimate the relative impact of the profiles using a readily available statistic and there were disciplinary differences in the impact of the different kinds of profiles. Most social web profiles had some evidence of uptake, if not impact; nevertheless, the value of the indicators used is unclear.}, author = {Mas-Bleda, Amalia and Thelwall, Mike and Kousha, Kayvan and Aguillo, IsidroF.}, doi = {10.1007/s11192-014-1345-0}, interhash = {5110401b47f90128cbe885cf441ab7fb}, intrahash = {9fa40f587b142513785037b67040abe4}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 1, pages = {337-356}, publisher = {Springer Netherlands}, title = {Do highly cited researchers successfully use the social web?}, url = {http://dx.doi.org/10.1007/s11192-014-1345-0}, volume = 101, year = 2014 } @article{thelwall2013altmetrics, abstract = {

Altmetric measurements derived from the social web are increasingly advocated and used as early indicators of article impact and usefulness. Nevertheless, there is a lack of systematic scientific evidence that altmetrics are valid proxies of either impact or utility although a few case studies have reported medium correlations between specific altmetrics and citation rates for individual journals or fields. To fill this gap, this study compares 11 altmetrics with Web of Science citations for 76 to 208,739 PubMed articles with at least one altmetric mention in each case and up to 1,891 journals per metric. It also introduces a simple sign test to overcome biases caused by different citation and usage windows. Statistically significant associations were found between higher metric scores and higher citations for articles with positive altmetric scores in all cases with sufficient evidence (Twitter, Facebook wall posts, research highlights, blogs, mainstream media and forums) except perhaps for Google+ posts. Evidence was insufficient for LinkedIn, Pinterest, question and answer sites, and Reddit, and no conclusions should be drawn about articles with zero altmetric scores or the strength of any correlation between altmetrics and citations. Nevertheless, comparisons between citations and metric values for articles published at different times, even within the same year, can remove or reverse this association and so publishers and scientometricians should consider the effect of time when using altmetrics to rank articles. Finally, the coverage of all the altmetrics except for Twitter seems to be low and so it is not clear if they are prevalent enough to be useful in practice.

}, author = {Thelwall, Mike and Haustein, Stefanie and Larivière, Vincent and Sugimoto, Cassidy R.}, doi = {10.1371/journal.pone.0064841}, interhash = {e8b290200bf83fcd1720e59253febd92}, intrahash = {cefe270b61c929ee0fff81d36cedf87a}, journal = {PLoS ONE}, month = {05}, number = 5, pages = {e64841}, publisher = {Public Library of Science}, title = {Do Altmetrics Work? Twitter and Ten Other Social Web Services}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0064841}, volume = 8, year = 2013 } @article{nanba2000classification, abstract = {We are investigating automatic generation of a review (or survey) article in a specific subject domain. In a research paper, there are passages where the author describes the essence of a cited paper and the differences between the current paper and the cited paper (we call them citing areas). These passages can be considered as a kind of summary of the cited paper from the current author's viewpoint. We can know the state of the art in a specific subject domain from the collection of citing areas. FUrther, if these citing areas are properly classified and organized, they can act 8.', a kind of a review article. In our previous research, we proposed the automatic extraction of citing areas. Then, with the information in the citing areas, we automatically identified the types of citation relationships that indicate the reasons for citation (we call them citation types). Citation types offer a useful clue for organizing citing areas. In addition, to support writing a review article, it is necessary to take account of the contents of the papers together with the citation links and citation types. In this paper, we propose several methods for classifying papers automatically. We found that our proposed methods BCCT-C, the bibliographic coupling considering only type C citations, which pointed out the problems or gaps in related works, are more effective than others. We also implemented a prototype system to support writing a review article, which is based on our proposed method.}, author = {Nanba, H. and Kando, N. and Okumura, M.}, interhash = {a8fbc36d3ee8de28f65ef2486bb18cd2}, intrahash = {7a99ee2d1444ae569beb7bee04137e4b}, journal = {11th ASIS SIG/CR Classification Research Workshop}, misc = {10.7152/acro.v11i1.12774}, pages = {117--134}, title = {Classification of research papers using citation links and citation types: Towards automatic review article generation}, url = {http://journals.lib.washington.edu/index.php/acro/article/download/12774/11255}, year = 2000 } @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 } @inproceedings{brookes1990biblio, abstract = {This paper traces the origins of informatics, scientometrics and informetrics in the USSR and Hungary; the origins of information science, information studies and bibliometrics in Britain and the USA, and their interactions with library studies. Finaly, three different contexts are suggested in which the three '-metrics' have distinctive and important rules.}, author = {Brookes, Bertram Claude}, booktitle = {nformetrics 89/90 : selection of papers submitted for the second International Conference on Bibliometrics, Scientometrics, and Informetrics, London, Ontario, Canada, 5-7 July 1989}, editor = {Egghe, Leo and Rousseau, Ronald}, id = {0-444-88460-2, http://hdl.handle.net/1942/857}, interhash = {f6654c8d4f1c4e5db5a961e117a975ac}, intrahash = {3af628d72fd5c44ae741d4571463b1ac}, publisher = {Elsevier}, title = {Biblio-, Sciento-, Infor-metrics??? What are we talking about?}, type = {Proceedings Paper}, url = {https://uhdspace.uhasselt.be/dspace/handle/1942/857}, year = 1990 } @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 = {http://dx.doi.org/10.1016/j.joi.2011.04.002}, interhash = {13fe59aae3d6ef95b529ffe00ede4126}, intrahash = {c3e49ee7b0ed81ecd126d3ef76d5f407}, 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{taguesutcliffe1992introduction, abstract = {The scope and significance of the field of informetrics is defined and related to the earlier fields of bibliometrics and scientometrics. The phenomena studied by informetricians are identified. The major contributors to the field in the past are described and current emphases are related to the contributions in this Special Issue. }, author = {Tague-Sutcliffe, Jean}, doi = {http://dx.doi.org/10.1016/0306-4573(92)90087-G}, interhash = {d0f741e4634dc9881dcf19bd241844b4}, intrahash = {6573cd7245e0df2962b54440b99608da}, issn = {0306-4573}, journal = {Information Processing & Management }, number = 1, pages = {1 - 3}, title = {An introduction to informetrics }, url = {http://www.sciencedirect.com/science/article/pii/030645739290087G}, volume = 28, year = 1992 } @article{aksnes2003macro, abstract = {This study investigates the role of self-citation in the scientific production of Norway (1981-1996). More than 45,000 publications have been analysed. Using a three-year citation window we find that 36% of all citations represent author self-citations. However, this percentage is decreasing when citations are traced for longer periods. We find the highest share of self-citation among the least cited papers. There is a strong positive correlation between the number of self-citations and the number of authors of the publications. Still, only a minor part of the overall increase in citation rates that can be found for multi-authored papers is due to self-citations. Also, the share of self-citation shows significant variations among different scientific disciplines. The results are relevant for the discussion concerning use of citation indicators in research assessments.}, author = {Aksnes, Dag W.}, doi = {10.1023/A:1021919228368}, interhash = {d929a33337bbbcc3b3bc1ebc2d1fe3d3}, intrahash = {b76d63d366bb3ca267df1c4f92979b69}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 2, pages = {235-246}, publisher = {Kluwer Academic Publishers}, title = {A macro study of self-citation}, url = {http://dx.doi.org/10.1023/A%3A1021919228368}, volume = 56, year = 2003 } @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 }