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

3 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 R2 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 R2 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. The online attention received by published articles can be indicative of future citation count and therefore scientific impact. The #Uro_SoMe_Score is specific for the urological literature and can be used for this purpose.

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

PubMed: MeSH publication types

  • Journal Article


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