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https://ahro.austin.org.au/austinjspui/handle/1/22074| Title: | Early Online Attention Can Predict Citation Counts for Urological Publications: The #UroSoMe_Score. | Austin Authors: | Sathianathen, Niranjan J;Lane, Robert;Condon, Benjamin;Murphy, Declan G;Lawrentschuk, Nathan;Weight, Christopher J;Lamb, Alastair D | Affiliation: | Department of Urology, University of Minnesota, Minneapolis, MN, USA Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia Department of Urology, University of Minnesota, Minneapolis, MN, USA Department of Urology, Churchill Hospital Cancer Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia Department of Surgery, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia |
Issue Date: | 15-May-2020 | Date: | 2019-11-05 | Publication information: | European Urology Focus 2020; 6(3): 458-462 | Abstract: | 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. To develop a urology social media score (#UroSoMe_Score) to predict citation counts from measures of online attention for urological articles. 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. 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. 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. The #UroSoMe_Score can be used to predict 2-yr citation counts for urological publications on the basis of online metrics. 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. | URI: | https://ahro.austin.org.au/austinjspui/handle/1/22074 | DOI: | 10.1016/j.euf.2019.10.015 | ORCID: | 0000-0001-8553-5618 | Journal: | European Urology Focus | PubMed URL: | 31704280 | Type: | Journal Article | Subjects: | Social media citation analysis |
| Appears in Collections: | Journal articles |
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