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Can Twitter increase the visibility of Chinese publications?

Author

Listed:
  • Fei Shu

    (McGill University)

  • Wen Lou

    (East China Normal University)

  • Stefanie Haustein

    (University of Ottawa)

Abstract

The purpose of this study is to investigate whether diffusion through social media can help to improve the international visibility of Chinese papers and thus increase their citation impact. After analysing 160,233 Chinese papers published in 2012, as well as the number of tweets and citations received, the results indicate that tweeted Chinese papers published in the same year and journal received around 15% more citations than Chinese papers not mentioned on Twitter. The citation advantage of tweeted Chinese papers is also found within various disciplines and by the different citing countries.

Suggested Citation

  • Fei Shu & Wen Lou & Stefanie Haustein, 2018. "Can Twitter increase the visibility of Chinese publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 505-519, July.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:1:d:10.1007_s11192-018-2732-8
    DOI: 10.1007/s11192-018-2732-8
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    References listed on IDEAS

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    2. Zhichao Fang & Rodrigo Costas & Wencan Tian & Xianwen Wang & Paul Wouters, 2021. "How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 918-932, July.
    3. Kong, Ling & Wang, Dongbo, 2020. "Comparison of citations and attention of cover and non-cover papers," Journal of Informetrics, Elsevier, vol. 14(4).
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    6. Liwei Zhang & Jue Wang, 2018. "Why highly cited articles are not highly tweeted? A biology case," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 495-509, October.
    7. Yaxue Ma & Zhichao Ba & Yuxiang Zhao & Jin Mao & Gang Li, 2021. "Understanding and predicting the dissemination of scientific papers on social media: a two-step simultaneous equation modeling–artificial neural network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7051-7085, August.
    8. Saeed-Ul Hassan & Naif R. Aljohani & Mudassir Shabbir & Umair Ali & Sehrish Iqbal & Raheem Sarwar & Eugenio Martínez-Cámara & Sebastián Ventura & Francisco Herrera, 2020. "Tweet Coupling: a social media methodology for clustering scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 973-991, August.
    9. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.

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