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Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media

Author

Listed:
  • Mojisola Erdt

    (Nanyang Technological University)

  • Aarthy Nagarajan

    (Nanyang Technological University)

  • Sei-Ching Joanna Sin

    (Nanyang Technological University)

  • Yin-Leng Theng

    (Nanyang Technological University)

Abstract

Altmetrics is an emergent research area whereby social media is applied as a source of metrics to assess scholarly impact. In the last few years, the interest in altmetrics has grown, giving rise to many questions regarding their potential benefits and challenges. This paper aims to address some of these questions. First, we provide an overview of the altmetrics landscape, comparing tool features, social media data sources, and social media events provided by altmetric aggregators. Second, we conduct a systematic review of the altmetrics literature. A total of 172 articles were analysed, revealing a steady rise in altmetrics research since 2011. Third, we analyse the results of over 80 studies from the altmetrics literature on two major research topics: cross-metric validation and coverage of altmetrics. An aggregated percentage coverage across studies on 11 data sources shows that Mendeley has the highest coverage of about 59 % across 15 studies. A meta-analysis across more than 40 cross-metric validation studies shows overall a weak correlation (ranging from 0.08 to 0.5) between altmetrics and citation counts, confirming that altmetrics do indeed measure a different kind of research impact, thus acting as a complement rather than a substitute to traditional metrics. Finally, we highlight open challenges and issues facing altmetrics and discuss future research areas.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:scient:v:109:y:2016:i:2:d:10.1007_s11192-016-2077-0
    DOI: 10.1007/s11192-016-2077-0
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