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CiteULike bookmarks are correlated to citations at journal and author levels in library and information science

Author

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  • Hajar Sotudeh

    (Shiraz University)

  • Zahra Mazarei

    (Shiraz University)

  • Mahdieh Mirzabeigi

    (Shiraz University)

Abstract

Aiming to explore the applicability of bookmarking data in measuring the scientific impact, the present study investigates the correlation between conventional impact indicators (i.e. impact factors and mean citations) and bookmarking metrics (mean bookmarks and percentage of bookmarked articles) at author and journal aggregation levels in library and information science (LIS) field. Applying the citation analysis method, it studies a purposeful sample of LIS articles indexed in SSCI during 2004–2012 and bookmarked in CiteULike. Data are collected via WoS, Journal Citation Report, and CiteULike. There is a positive, though weak, correlation between LIS authors’ mean citations and their mean bookmarks, as well as a moderate to large correlation between LIS journals’ impact factors on the one hand and on the other, their mean bookmarks, and the percentage of their bookmarked articles. Given the correlation between the citation- and bookmark-based indicators at author and journal levels, bookmarking data can be used as a complement to, but not a substitute for, the traditional indicators to get to a more inclusive evaluation of journals and authors.

Suggested Citation

  • Hajar Sotudeh & Zahra Mazarei & Mahdieh Mirzabeigi, 2015. "CiteULike bookmarks are correlated to citations at journal and author levels in library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2237-2248, December.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1745-9
    DOI: 10.1007/s11192-015-1745-9
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    References listed on IDEAS

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    1. Xuemei Li & Mike Thelwall & Dean Giustini, 2012. "Validating online reference managers for scholarly impact measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 461-471, May.
    2. Zohreh Zahedi & Rodrigo Costas & Paul Wouters, 2014. "How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1491-1513, November.
    3. Bornmann, Lutz, 2014. "Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics," Journal of Informetrics, Elsevier, vol. 8(4), pages 895-903.
    4. Mike Thelwall, 2012. "Journal impact evaluation: a webometric perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 429-441, August.
    5. Ehsan Mohammadi & Mike Thelwall, 2014. "Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(8), pages 1627-1638, August.
    6. Lutz Bornmann, 2015. "Alternative metrics in scientometrics: a meta-analysis of research into three altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1123-1144, June.
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    Cited by:

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    3. Banshal, Sumit Kumar & Gupta, Solanki & Lathabai, Hiran H & Singh, Vivek Kumar, 2022. "Power Laws in altmetrics: An empirical analysis," Journal of Informetrics, Elsevier, vol. 16(3).
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    5. Xianwen Wang & Zhichao Fang & Xinhui Guo, 2016. "Tracking the digital footprints to scholarly articles from social media," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1365-1376, November.
    6. Mojisola Erdt & Aarthy Nagarajan & Sei-Ching Joanna Sin & Yin-Leng Theng, 2016. "Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1117-1166, November.

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