-
Sugimoto, C.R.; Work, S.; Larivière, V.; Haustein, S.: Scholarly use of social media and altmetrics : A review of the literature (2017)
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- Abstract
- Social media has become integrated into the fabric of the scholarly communication system in fundamental ways, principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics-that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of 2 main parts: the first examines the use of social media in academia, reviewing the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.
- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2037-2062
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Nicolaisen, J.: Citation analysis (2007)
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- Date
- 13. 7.2008 19:53:22
- Source
- Annual review of information science and technology. 41(2007), S.xxx-xxx
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Borgman, C.L.; Furner, J.: Scholarly communication and bibliometrics (2002)
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- Abstract
- Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990-2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments.
- Source
- Annual review of information science and technology. 36(2002), S.3-72
-
Thelwall, M.; Vaughan, L.; Björneborn, L.: Webometrics (2004)
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- Abstract
- Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.
- Source
- Annual review of information science and technology. 39(2005), S.81-138
-
Narin, F.; Moll, J.K.: Bibliometrics (1977)
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- Source
- Annual review of information science and technology. 12(1977), S.35-58
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Hjerppe, R.: ¬An outline of bibliometrics and citation analysis (1980)
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- Imprint
- Stockholm : Royal Institute of Technology Library
-
Bensman, S.J.: Garfield and the impact factors (2007)
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- Source
- Annual review of information science and technology. 41(2007), S.xxx-xxx
-
Kinnucan, M.T.: Statistical methods in information science research (1986)
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- Source
- Annual review of information science and technology. 21(1986), S.147-178
-
White, H.D.; McCain, W.: Bibliometrics (1989)
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- Source
- Annual review of information science and technology. 24(1989), S.119-186
-
Kurtz, M.; Bollen, J.: Usage bibliometrics (2010)
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- Source
- Annual review of information science and technology. 44(2010), S.xxx-xxx
-
Egghe, L.: ¬The Hirsch index and related impact measures (2010)
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- Source
- Annual review of information science and technology. 44(2010) no.1, S.65-114
