Have a personal or library account? Click to login

Library and Information Science Papers Discussed on Twitter: A new Network-based Approach for Measuring Public Attention

Open Access
|Jul 2020

References

  1. Bornmann, L. (2015). Alternative metrics in scientometrics: A meta-analysis of research into three altmetrics. Scientometrics, 103(3), 1123–1144. doi: 10.1007/s11192-015-1565-y.
  2. Bornmann, L., & Haunschild, R. (2016). How to normalize Twitter counts? A first attempt based on journals in the Twitter Index. Scientometrics, 107(3), 1405–1422. doi: 10.1007/s11192-016-1893-6.
  3. Bornmann, L., Haunschild, R., & Adams, J. (2019). Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF). Journal of Informetrics, 13(1), 325–340. doi: 10.1016/j.joi.2019.01.008.
  4. Bornmann, L., Haunschild, R., & Marx, W. (2016). Policy documents as sources for measuring societal impact: How often is climate change research mentioned in policy-related documents? Scientometrics, 109(3), 1477–1495. doi: 10.1007/s11192-016-2115-y.
  5. Haunschild, R., Leydesdorff, L., & Bornmann, L. (2019). Library and Information Science papers as topics on Twitter: A network approach to measuring public attention. Paper presented at the ISSI 2019—17th International Conference of the International Society for Scientometrics and Informetrics, Rome, Italy.
  6. Haunschild, R., Leydesdorff, L., Bornmann, L., Hellsten, I., & Marx, W. (2019). Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags. Journal of Informetrics, 13(2), 695–707. doi: 10.1016/j.joi.2019.03.008.
  7. Haunschild, R., Leydesdorff, L., Bornmann, L., Hellsten, I., & Marx, W. (2020). Corrigendum to “Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags” [J. Informetrics 13 (2019) 695–707]. Journal of Informetrics, 14(1), February 2020, 101020. doi: 10.1016/j.joi.2020.101020
  8. Hellsten, I., & Leydesdorff, L. (2020). Automated analysis of actor–topic networks on twitter: New approaches to the analysis of socio-semantic networks. JASIST, 71(1), 3–15. doi: 10.1002/asi.24207
  9. R Core Team. (2019). R: A Language and Environment for Statistical Computing (Version 3.6.0). Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.r-project.org/
  10. Robinson-Garcia, N., Costas, R., Isett, K., Melkers, J., & Hicks, D. (2017). The unbearable emptiness of tweeting—About journal articles. PLOS ONE, 12(8), e0183551. doi: 10.1371/journal.pone.0183551.
  11. Thelwall, M. (2018). Early Mendeley readers correlate with later citation counts. Scientometrics, 115(3), 1231–1240. doi: 10.1007/s11192-018-2715-9.
  12. Wouters. P., Zahedi, Z., & Costas, R. (2019) Social media metrics for new research evaluation. In: Glänzel W., Moed H.F., Schmoch U., Thelwall M. (eds) Springer Handbook of Science and Technology Indicators. Springer Handbooks. Springer, Cham, pp 687–713.
DOI: https://doi.org/10.2478/jdis-2020-0017 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Language: English
Page range: 5 - 17
Submitted on: Jan 21, 2020
Accepted on: May 20, 2020
Published on: Jul 3, 2020
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2020 Robin Haunschild, Loet Leydesdorff, Lutz Bornmann, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.