Early Online Attention Can Predict Citation Counts for Urological Publications: The #UroSoMe_Score

Niranjan J. Sathianathen, Robert Lane, Benjamin Condon, Declan G. Murphy, Nathan Lawrentschuk, Christopher J. Weight, Alastair D. Lamb

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


BACKGROUND: The scientific impact of published articles has traditionally been measured as citation counts. However, there has been a shift in academia to a digitalized age in which research is widely read, disseminated, and discussed online. As part of this shift, each published article has a digital footprint.

OBJECTIVE: To develop a urology social media score (#UroSoMe_Score) to predict citation counts from measures of online attention for urological articles.

DESIGN, SETTING, AND PARTICIPANTS: We included articles published between June 2016 and June 2017 in the top ten highest-impact urology journals. We obtained data on the online attention received by each of these articles from Altmetric Explorer and 2-yr citation counts from Scopus.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We created a multivariable linear model using the forward stepwise regression method based on the Akaike information criterion to determine the best-fitting model using online sources of attention to predict 2-yr citation count.

RESULTS AND LIMITATIONS: We included a total of 2033 urology articles. The median weighted Altmetric score for the articles included was 4 (interquartile range [IQR] 2-11). The median number of citations for all articles included was 7 (IQR 3-14). There was an association between Altmetric score and 2-yr Scopus citation count (p < 0.001) but the adjusted R 2 value for this model was only 0.013. Our stepwise regression model revealed that citations could be predicted from a model comprising the following sources of online attention: policy documents, Google+, blogs, videos, Wikipedia, Twitter, and Q&A. The adjusted R 2 value for the #UroSoMe_Score model was 0.14, which is superior to the full Altmetric score.

CONCLUSIONS: The #UroSoMe_Score can be used to predict 2-yr citation counts for urological publications on the basis of online metrics.

PATIENT SUMMARY: Online measures of attention can be used to predict citation counts and thus the scientific impact of an article. Our #UroSoMe_Score can be used in such a manner specifically for the urological literature. Outliers may still be present especially for popular topics that receive online attention but are not heavily cited.

Original languageEnglish (US)
Pages (from-to)458-462
Number of pages5
JournalEuropean Urology Focus
Issue number3
StatePublished - May 15 2020

Bibliographical note

Publisher Copyright:
© 2019


  • Social media
  • citation analysis


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