Mendeley Readers Research Articles

Measuring the global scientific literature on big data analytics in the LIS: the blending approach of altmetrics and bibliometrics

Purpose This study aims to measure scientific literature on the emerging research area of “big data” in the field of “library and information science” (LIS). Design/methodology/approach This study used the “bibliometric method” for data curation. Web of Science and altmetric.com were used. Data analysis and visualisation were done using three widely used powerful data analytics software, R-bibliometrix, VOSviewer and Statistical Package for Social Sciences. Findings This study revealed the most preferred venues for publication. Furthermore, this study highlighted an association between the Mendeley readers of publications and citations. Furthermore, it was evident that the overall altimetric attention score (AAS) does not influence the citation score of publications. Other fascinating findings were moderate collaboration patterns overall. Furthermore, the study highlighted that big data (BD) research output and scientific influence in the LIS sector are continually increasing. Practical implications Findings related to BD analytics in LIS techniques can serve as helpful information for researchers, practitioners and policymakers. Originality/value This study contributes to the current knowledge accumulation by its unique manner of blending the two approaches, bibliometrics and altmetrics.

How far do oral lectures at European Orthodontic Society congresses reach? A comparison of abstract publication rates, article citations, and social media mentions.

This study investigated the scientific impact of oral abstracts presented in five consecutive European Orthodontic Society (EOS) congresses in terms of full-text publication rates as well as citations and social media metrics (altmetrics) of the resulting articles. PubMed (http://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar (http://scholar.google.nl/) were screened to identify published articles originally presented as lectures at EOS 2015-2019. EOS date, abstract title, subject, number of authors, authors' names, first author's country of origin, and type of affiliation were extracted from congress abstract books. Altmetric Attention Scores (AASs), number of X (formerly Twitter) posts, Mendeley reads, and citations of the articles were retrieved from Altmetric Explorer and Web of Science (WoS) databases, respectively. Abstract details, full publication rates and Altmetric mentions and WoS citations of the related articles were analysed using univariable and multivariable logistic regression. In total, 51.79 to 73.91% of the lectures presented at EOS 2015-2019 were converted into peer-reviewed articles. There was a median interval of 10.95 months (interquartile range 20.5 months) between conference and publication dates. EOS congress year (overall Wald test P-value = .04) and number of authors (overall Wald test P-value < .01) were significant predictors for full-text publication of oral lectures. There was no significant effect of EOS presentation on AASs, citations, X posts, and Mendeley readers (P > .05). Journal impact factors and quartile rankings were not considered to determine the impact of the journals that published articles originally presented as EOS oral abstracts. Overall, 61.30% of EOS 2015-2019 lectures were published as full-length articles. Multi-authored abstracts presented higher odds of publication, whereas oral abstracts presented at EOS 2015 and 2018 had the lowest probabilities to reach full publication. EOS abstract-based articles were assigned similar numbers of citations and AASs to articles not presented at EOS congresses.

Altmetric.com or PlumX: Does it matter?

AbstractAltmetric.com and PlumX are two prominent tools for collecting alternative metrics data. This study has two main objectives: first, to evaluate how the choice between Altmetric.com and PlumX affects the results of alternative metrics analysis, and second, to investigate the social impact of ‘hot papers’ through the alternative metrics data provided by these platforms. We employed a descriptive and exploratory approach, gathering common alternative metrics from 4236 hot papers using both Altmetric.com and PlumX. The data collected included various alternative metrics such as policy mentions, Mendeley readers, Wikipedia mentions, blog mentions, Facebook mentions, and news mentions, in addition to citation counts from Scopus. We conducted descriptive statistics and inferential analyses to examine the relationships between citations and alternative metrics, as well as to compare the data obtained from both platforms. Our findings indicate that PlumX has broader coverage of hot papers compared to Altmetric.com. While the mean and individual values of alternative metrics differ between the two platforms, the median and geometric mean are similar. Both Altmetric.com and PlumX demonstrate that publications with higher citation counts tend to receive more online attention. Notably, all alternative metrics for Immunology and Chemistry showed statistically significant differences between the two platforms, whereas in Mathematics, alternative metrics (with the exception of Mendeley readers) did not exhibit significant differences. The findings suggest that researchers should be aware of potential variations in data captured by different alternative metrics platforms. Additionally, interpreting alternative metrics data requires caution, considering the research fields and the specific platform used.

Five decades of research on immediate implant therapy: A modern bibliometric network analysis via Altmetric and level of evidence mapping.

To conduct a bibliometric network analysis to explore the research landscape of immediate implant placement (IIP) and provide insights into its trends and contributors. The Scopus database was utilized as the bibliographic source, and a search strategy was implemented to identify relevant research articles. Various bibliometric parameters were extracted, including publication year, journal, authors, citations, and funding. The analysis involved examining authorship patterns, international collaborations, level of evidence, Altmetric data, and funding analysis. We identified a steady annual growth rate of 6.49% in IIP research. The top three countries contributing to research output were the USA, Italy, and China. Prolific authors were identified based on publication and citation metrics. International collaborations among different countries were observed. The level of evidence analysis revealed that over 30% of the articles fell into higher levels of evidence (levels 1 and 2). Altmetric data analysis indicated no significant correlations between citation counts and Altmetric Attention Score (AAS), and conversely a significant association with Mendeley readers count. Funding and open access did not significantly impact the bibliometric indices of the papers. The focus of research on IIP has been evolving as indicated by an exponential growth rate in this study. Only approximately 16% of the articles fit into level 1 evidence, therefore, emphasizing on higher quality level research study shortage in this field. Modern indices can be used as new bibliometric indicators as they also cover social media and online attention scores.

Can ChatGPT be used to predict citation counts, readership, and social media interaction? An exploration among 2222 scientific abstracts

This study explores the potential of ChatGPT, a large language model, in scientometrics by assessing its ability to predict citation counts, Mendeley readers, and social media engagement. In this study, 2222 abstracts from PLOS ONE articles published during the initial months of 2022 were analyzed using ChatGPT-4, which used a set of 60 criteria to assess each abstract. Using a principal component analysis, three components were identified: Quality and Reliability, Accessibility and Understandability, and Novelty and Engagement. The Accessibility and Understandability of the abstracts correlated with higher Mendeley readership, while Novelty and Engagement and Accessibility and Understandability were linked to citation counts (Dimensions, Scopus, Google Scholar) and social media attention. Quality and Reliability showed minimal correlation with citation and altmetrics outcomes. Finally, it was found that the predictive correlations of ChatGPT-based assessments surpassed traditional readability metrics. The findings highlight the potential of large language models in scientometrics and possibly pave the way for AI-assisted peer review.

Identifying Combinations of Altmetrics and Web of Science Usage That Linked to Early Citations of an Article Received: A Crisp-Set Qualitative Comparative Analysis (csQCA)

The relations of altmetrics/usage of an article and its citations have been studied extensively by using several variable-oriented approaches, e.g., correlations analysis, regression analysis, and so on. Variable-oriented approaches are symmetrically designed to estimate the “net effects” of independent variables on outcomes. In contrast to variable-oriented approaches, Qualitative Comparative Analysis (QCA), a case-oriented approach based on set theory, models the concept of conjunctural causation, rather than a single net effect. QCA assumes asymmetry, equifinality, and causal complexity. In this study, based on four data sets from Scientific Reports, a Crisp-set Qualitative Comparative Analysis (csQCA) was employed to identify combinations of altmetrics and Web of Science usage (WOSusage) that linked to early citations an article received. Our results revealed the existence of diverse combinations of altmetrics and WOSusage that linked to early citations. The four combinations with the highest raw coverage for each data set indicated that high Mendeley readers together with high WOSusage played a key role in early citations an article received. The other altmetrics, including Twitter, Facebook, blogs, and news, played roles in linking to early citations an article received but varied in different combinations for each data set.

Altmetrics can capture research evidence: an analysis across types of studies in COVID-19 literature

COVID-19 has greatly impacted science. It has become a global research front that constitutes a unique phenomenon of interest for the scientometric community. Accordingly, there has been a proliferation of descriptive studies on COVID-19 papers using altmetrics. Social media metrics serve to elucidate how research is shared and discussed, and one of the key points is to determine which factors are well-conditioned altmetric values. The main objective of this study is to analyze whether the altmetric mentions of COVID-19 medical studies are associated with the type of study and its level of evidence. Data were collected from the PubMed and Altmetric.com databases. A total of 16,672 publications by study types (e.g., case reports, clinical trials, or meta-analyses) that were published in the year 2021 and that had at least one altmetric mention were retrieved. The altmetric indicators considered were Altmetric Attention Score (AAS), news mentions, Twitter mentions, and Mendeley readers. Once the dataset of COVID-19 had been created, the first step was to carry out a descriptive study. Then, a normality hypothesis was evaluated by means of the Kolmogorov–Smirnov test, and since this was significant in all cases, the overall comparison of groups was performed using the nonparametric Kruskal–Wallis test. When this test rejected the null hypothesis, pairwise comparisons were performed with the Mann–Whitney U test, and the intensity of the possible association was measured using Cramer’s V coefficient. The results suggest that the data do not fit a normal distribution. The Mann–Whitney U test revealed coincidences in five groups of study types: The altmetric indicator with most coincidences was news mentions, and the study types with the most coincidences were the systematic reviews together with the meta-analyses, which coincided with four altmetric indicators. Likewise, between the study types and the altmetric indicators, a weak but significant association was observed through the chi-square and Cramer’s V. It can thus be concluded that the positive association between altmetrics and study types in medicine could reflect the level of the “pyramid” of scientific evidence.

Bibliometric and altmetric analysis of research relating to antiphospholipid syndrome based on VOS viewer (2011-2021).

Antiphospholipid syndrome is an autoimmune disorder characterized by recurrent vascular thrombosis and pregnancy losses in the presence of persistently positive antiphospholipid antibodies. Bibliometric analysis and altmetric analysis are methods of analyzing academic productivity and influence. Currently, the assessment of antiphospholipid syndrome through the above analyses is lacking. This study investigated the quantity and quality of studies in the field of antiphospholipid syndrome and revealed the characteristics of worldwide productivity on this disease by the bibliometric analysis and altmetric analysis. The terms "APS," "antiphospholipid syndrome," "antiphospholipid-antibody syndrome," and "Hughes syndrome" were searched on the Web of Science Core Collection from January 1, 2011, to December 31, 2021. Original articles and reviews were selected. We set the filters as "English." A total of 1818 articles were retrieved from 68 countries, of which 20 met the criteria of major active countries. High-income countries contributed 1341 articles (73.48%). The number of articles annually increased significantly in the 10-year period (P < 0.001). The USA (253, 13.91%) was the most productive country. Adjusted by population, Serbia was top of the list. According to the gross domestic product analysis, Serbia ranked first. The most used keywords such as thrombosis and antiphospholipid antibodies were presented by keywords analyses. A content analysis found antithrombotic and anticoagulant therapy as research hotspots. A significant correlation was detected between average citations and altmetric attention scores (P = 0.002) and Mendeley readers count (P < 0.001). From 2011 to 2021, the number of global articles increased rapidly. Most papers came from high-income countries. The relationship between the bibliometric and altmetric analyses were basically consistent; therefore the two can prove/complement each other. Key points • We revealed the global productivity characteristics of the papers related to antiphospholipid syndrome by using the methods of bibliometric analysis and altmetric analysis. • We found the most selected articles that describe the treatment of antiphospholipid syndrome, especially antithrombotic and anticoagulant treatments, which may be the current research hotspot.

Impact of COVID-19 Dentistry-Related Literature: An Altmetric Study

Scientific literature on COVID-19 has grown rapidly during the pandemic. The aim of this study was to provide a comprehensive overview of the popularity on the web of the available dental publications on COVID-19 and to examine associations amongst article characteristics, online mentions, and citations. An Altmetric Explorer search was conducted for COVID-19 articles published in dental journals using 3 keywords: COVID-19, SARS-CoV-2, and pandemic. The following Altmetric data were collected: Altmetric attention score (AAS), mentions by news outlets, tweets, Mendeley readers, and Web of Science citations. Additionally, article title, type, topic, origin and open access status, journal title, quartile of impact factor (IF) distribution, and time lapse between COVID-19 pandemic onset and publication date were analysed. In all, 253 articles published in 48 dental journals were eligible for the study. AAS was significantly influenced by article topic, type, origin, and journal IF quartile. There was a negligible correlation between AAS and Web of Science citations. Mendeley was the only Altmetric source highly correlated with citations. There was substantial online interest in COVID-19 dentistry-related literature, as depicted by the AAS of the reviewed articles and social media metrics. Mendeley reader counts were highly correlated with citations, and they may therefore be valuable in research impact evaluation.

Bibliometric and altmetric analyses of publication activity in the field of idiopathic inflammatory myopathy in 2011-2021 based on web of science core collection databases.

Idiopathic inflammatory myopathy is a multisystem autoimmune condition characterized by muscle inflammation (myositis) and interstitial lung disease (ILD). Bibliometric analysis and altmetric analysis are effective methods of evaluating academic productivity and measuring the influence of publications. The purpose of this study is to analyze the research productivity and influence of idiopathic inflammatory myopathy articles using bibliometric analysis and altmetric analysis. Data from articles published between 2011 and 2021 were obtained from the Web of Science. Altmetric attention scores and Mendeley reader counts of the articles were obtained from altmetric evaluations. In the study, 2060 articles related to idiopathic inflammatory myopathy were screened out showing an increasing trend in general. In terms of the origin countries of articles, the United States (n = 467, 22.67%) ranked first. Johns Hopkins University has the largest number of institutions (n = 113). The journals regarding idiopathic inflammatory myopathy and Rheumatology (n = 87) published the most articles. The most cited article was published by Mammen et al. and was related to autoantibodies. Dermatomyositis, polymyositis and other research hotspots were represented by keywords. The results of the altmetric analysis showed that citations, impact factors and h-index were significantly correlated with Altmetric Attention Scores and Mendeley Readers Count (P < 0.05). In summary, bibliometric analysis summarizes the current status of idiopathic inflammatory myopathy research and helps to understand the development of idiopathic inflammatory myopathy in the field between 2011 and 2021. Altmetric analysis was used to evaluate the academic and social influence of articles from the novel perspective of internet attention.

User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis

This study investigates the extent to which scholarly tweets of scientific papers are engaged with by Twitter users through four types of user engagement behaviors, i.e., liking, retweeting, quoting, and replying. Based on a sample consisting of 7 million scholarly tweets of Web of Science papers, our results show that likes is the most prevalent engagement metric, covering 44% of scholarly tweets, followed by retweets (36%), whereas quotes and replies are only present for 9% and 7% of all scholarly tweets, respectively. From a disciplinary point of view, scholarly tweets in the field of Social Sciences and Humanities are more likely to trigger user engagement over other subject fields. The presence of user engagement is more associated with other Twitter-based factors (e.g., number of mentioned users in tweets and number of followers of users) than with science-based factors (e.g., citations and Mendeley readers of tweeted papers). Building on these findings, this study sheds light on the possibility to apply user engagement metrics in measuring deeper levels of Twitter reception of scholarly information.

Mendeley Readers of Highly-Cited Articles in Medical Sciences: Is It Correlated With Citations?

The number of Mendeley readers and the number of citations are indicators of a research evaluation. In many cases, they have a significant correlation with each other. However, there is a minimal amount of evidence regarding the correlation between these two variables in highly-cited articles. The present study aimed to investigate the correlation between the number of Mendeley readers and the number of citations in highly-cited medical articles. The research population includes all highly-cited articles in various fields of medicine indexed in the Web of Science (WoS) in 2016. The number of citations was extracted from the WoS, and the number of Mendeley readers was extracted using the webometric analyst. Findings revealed that the most Mendeley readers are related to the field of general and internal medicine with an average of 570.43, and the lowest is dedicated to otorhinolaryngology with an average of 86.2. Also, the highest average citation belongs to general and internal medicine (338.18) and the lowest to nursing (40.84). Pearson correlation coefficient showed that the relationship between the number of Mendeley readers and the number of citations received by all medical articles is positive and significant (p-value <0.001, r = 0.644). In various fields of medicine, except orthopedics, there is a positive and significant relationship between two variables. Evidence suggests that Mendeley data can determine the effectiveness of articles. It seems that more researchers’ use of Mendeley will increase the visibility and readers of the articles and, as a result, will lead to more citations.

Coverage of Data Sources and Correlations Between Altmetrics and Citation Indicators: The Case of a Brazilian Portal of Open Access Journals

Analyzes the coverage of bibliometric and altmetric data sources, and the correlations between bibliometric citation indicators and altmetric indicators. The analyses were performed by area of knowledge, making it possible to identify differences between them. A total of 11,955 articles published in the journals of the Journal Portal of the Federal University of Goiás were analyzed. The main results were the high coverage of 95.7 and 96.7% found for the altmetric indicators of article downloads and accesses to the abstract, followed by 53.6% for Mendeley readers. The Pearson correlations between altmetric indicators and citation indicators vary from weak to moderate, the strongest correlations being those between the altmetric indicator of accesses to abstracts and the Google Scholar citations indicator (r = 0.475), and between Mendeley readers and Google Scholar citations (r = 0.467). Correlations between tweets and other altmetric indicators are always negative and very weak. The coverage of altmetric indicators varies among the areas, the highest values being found among Life Sciences. We concluded that the use of altmetric indicators, especially of Mendeley readers, can be considered complementary to bibliometric indicators.

How Does Open Access Publication Impact Readership and Citation Rates of Lumbar Spine Literature?

This was a retrospective review. The objective of this study was to assess the impact of open access (OA) publication on citation rates and attention scores of literature related to lumbar spine surgery. OA literature allows readers to view full-text manuscripts of research publications free of charge, however, OA publication is often associated with substantial fees for authors. The Altmetric database was searched for articles related to lumbar spine surgery. Title, journal, publication date, Dimensions Citations, Mendeley Readers, Altmetric Attention Score (AAS), number of public mentions, and OA status were collected for each included article. The influence of OA status on Dimensions Citations, Mendeley Readers, and each individual component of the AAS was assessed. To control for journal influence, impact of OA on Dimensions Citations and AAS was separately assessed for each of the top 10 journals contributing the most mentioned articles. The top 25 most cited articles and top 25 articles by AAS were also characterized. A total of 5245 articles were included, of which 2063 were published with OA and 3182 were not. OA status was a significant, independent predictor of AAS and Mendeley Readers (both P <0.001), but not Dimensions Citations ( P =0.422). OA status significantly predicted mentions in news stories ( P =0.003), Twitter posts ( P <0.001), Facebook posts ( P <0.001), and Wikipedia citations ( P =0.011). Of the top 10 contributing journals, OA status significantly predicted Dimensions Citations for European Spine Journal , Journal of Neurosurgery: Spine , and Neurosurgery ( P ≤0.005) and predicted AAS for Spine , European Spine Journal , The Spine Journal , Journal of Neurosurgery: Spine , and Neurosurgery ( P ≤0.017, all). OA status appeared to significantly impact public attention scores, but not citation rates, although these effects did vary based on the journal in which articles were published. Authors may want to consider OA publication based on their target audience and the goal of their research.

Does an Author's Social Media Presence Affect Dissemination of Spine Literature?

Our study assesses the impact of an author's social media presence on citation rates and readership of spine literature. The Altmetric database was queried for spine-related articles between 2016 and 2021; the top 100 by Altmetric Attention Score (AAS) were assessed. Public profile presence, number of followers, number of posts, and promotion of articles were assessed for Twitter/Instagram. Social media profiles were identified by searching for the author's name followed by "Twitter" or "Instagram" on Google.com or searching each platform. Descriptive statistics assessed social media use and attention metrics. Negative binomial regression assessed presence/promotion/number of followers/number of posts on Twitter/Instagram as predictors of Dimensions citation rates/AAS/Mendeley reader counts, while accounting for time passed since publication. Twitter promotion was noted for 9.0% of articles and Instagram promotion for 1.0%. Mean number of Twitter and Instagram followers was 447.9 ± 1406.1(range: 0-9079) and 173.2 ± 1097.1(range: 0:10,700), respectively. Mean number of Twitter and Instagram posts was 411.6 ± 1210.5 and 18.4 ± 96.4, respectively. Dimensions citations ranged from 0-641, AAS from 79-2257, and Mendeley readers from 2-1854. Following negative binomial regression, Instagram presence was identified as a significant predictor of Mendeley readers (P= 0.043), number of Twitter posts was a significant predictor of AAS (P= 0.008). Additionally, Twitter presence was identified as a negative predictor of Mendeley readers (P= 0.005) and Twitter promotion was identified as a negative predictor of AAS (P= 0.003). Activity on Twitter and Instagram may have variable associations with altmetrics of literature visibility and readership but with citation rates. Interestingly, presence/promotion on Twitter predicted less attention/readership, while Instagram presence predicted higher Mendeley readership.

Long-term Association of Altmetric Attention Scores With Citations in Selected Major Pharmacy Journals

Objective. To determine the long-term change in the Altmetric Attention Score (AAS) and its components, as well as the impact of higher AAS on citation count for articles published in major pharmacy journals.Methods. This study evaluated articles from pharmacy journals ranked in the top 10% according to their AAS in the year 2017. Correlation between the AAS and number of citations (through November 2020) was assessed using the Spearman's correlation test. A Kruskal-Wallis 1-way analysis of variance was used to compare the AAS across journals.Results. The median three-year AAS and citation count per article was 20 (25th, 75th percentile=15, 28) and 11 (6, 18), respectively. Between November 2018 and November 2020, there was no significant change in the median AAS for the 137 included articles. The only change in the AAS components was an increase in the number of Mendeley readers (22 [13, 34]). The median number of citations per article also increased (8 [4, 14]). We found a significant association between the three-year AAS and the three-year number of citations. The three-year number of Mendeley readers was associated with an increase in the 3-year number of citations. The mean three-year AAS was highest with articles published in the Journal of the American Pharmacists Association, while the mean three-year number of citations was highest for articles published in PharmacotherapyConclusion. Higher AAS scores appear to be associated with the number of citations for articles published in major pharmacy journals within three years of publication.

Outreach and Post-Publication Impact of Soil Erosion Modelling Literature

Back in the 1930s, the aphorism “publish or perish” first appeared in an academic context. Today, this phrase is becoming a harsh reality in several academic environments, and scientists are giving increasing attention to publishing and disseminating their scientific work. Soil erosion modelers make no exception. With the introduction of the bibliometric field, the evaluation of the impact of a piece of scientific work becomes more articulated. The post-publication impact of the research became an important aspect too. In this study, we analyse the outreach and the impact of the literature on soil erosion modelling using the altmetric database, i.e., Altmetric. In our analysis, we use only a small fraction (around 15%) of Global Applications of Soil Erosion Modelling Tracker (GASEMT) papers because only 257 papers out of 1697 had an Altmetric Score (AS) larger than 0. We observed that media and policy documents mentioned more frequently literature dealing with global-scale assessments and future projection studies than local-scale ones. Papers that are frequently cited by researchers do not necessarily also yield high media and policy outreach. The GASEMT papers that had an AS larger than 0 were, on average, mentioned by one policy document and five Twitter users and had 100 Mendeley readers. Only around 5% and 9% of papers with AS &gt; 0 appeared in news articles and blogs, respectively. However, this percentage was around 45% for Twitter and policy mentions. The top GASEMT paper’s upper bound was around 1 million Twitter followers, while this number was around 10,000 for the 10th ranked GASEMT paper. The exponentially increasing trend for erosion modelling papers having an AS has been confirmed, as during the last 3 years (2014–2017), we estimated that the number of entries had doubled compared to 2011–2014 and quadrupled if we compare it with 2008–2011.

Information and Scientific Impact of Advanced Therapies in the Age of Mass Media: Altmetrics-Based Analysis of Tissue Engineering.

BackgroundTissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-stage organs. Its development has taken place since the last decade of the 20th century, entailing a clinical revolution. TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact.ObjectiveThe objective of this study was to evaluate the online dimension of TE documents from 2012 to 2018 using metadata obtained from the Web of Science (WoS) and Altmetric and to develop a prediction equation for the impact of TE documents from altmetric scores.MethodsWe analyzed 10,112 TE documents through descriptive and statistical methods. First, the TE temporal evolution was exposed for WoS and 15 online platforms (news, blogs, policy, Twitter, patents, peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q&A, video, and Mendeley Readers). The 10 most cited TE original articles were ranked according to the normalized WoS citations and the normalized Altmetric Attention Score. Second, to better comprehend the TE online framework, correlation and factor analyses were performed based on the suitable results previously obtained for the Bartlett sphericity and Kaiser–Meyer–Olkin tests. Finally, the linear regression model was applied to elucidate the relation between academics and online media and to construct a prediction equation for TE from altmetrics data.ResultsTE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers, and Altmetric Scores. However, WoS and Altmetric rankings for the most cited documents clearly differ. When compared, the best correlation results were obtained for Mendeley Readers and WoS (ρ=0.71). In addition, the factor analysis identified 6 factors that could explain the previously observed differences between academic institutions and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than 40% of TE WoS citations from Altmetric scores.ConclusionsScientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because the focus of TE research importantly differs when the academic institutions and online platforms are compared, basic and clinical research groups, academic institutions, and health politicians should make a coordinated effort toward the design and implementation of adequate strategies for information diffusion and population health education.

EP.WE.372Has COVID-19 affected the attention surgical papers are receiving?

Abstract Aim Unsurprisingly, much of the medical profession has focussed on treating covid-19 over the past year often to the detriment of other pathologies. Engagement with academic literature may follow a similar trend; this paper used standard social media attention and citations metrices to assess whether the pandemic has affected engagement with surgical literature. Method Twitter Mentions and Mendeley Readers Data were retrieved for all papers published in Annals of Surgery, BJS and JAMASurgery between January 2019 and October 2020. The non-parametric Mann-Whitney U test was used to compare Twitter Mentions and Mendeley Readers for COVID surgical vs non-COVID surgical publications (in 2020) and all surgical papers published before and after the advent of COVID-19. A control database of all papers published in NEJM, BMJ and Lancet over the same period of time was also created. Results There was no difference in Twitter mentions between COVID-19 and non-COVID-19 papers (p-value 0.604); however there were significantly more Mendeley readers of COVID-19 papers than non-COVID-19 papers in 2020 (55 vs 5 median readers, p &amp;lt; 0.001). Surgical papers published in 2020 received significantly fewer Twitter mentions than those published in 2019 (15.5 vs 27, p &amp;lt; 0.001). Analysis of the non-surgical control dataset revealed the opposite trend; papers published in 2020 received significantly more attention than those published in 2019 (39 vs 30 median Twitter citations, p &amp;lt; 0.001). Conclusion Surgical papers published during the COVID-19 pandemic received significantly fewer social media mentions. Such reduced visibility has the potential to affect future citation metrics and dissemination of surgical knowledge

The Online Attention to Cleft Lip and Palate Research: An Altmetric Analysis.

To identify research articles related to cleft lip and/or cleft palate (CL/P) that generated the highest online attention. Altmetric Explorer was used to identify the 100 articles with the highest Altmetric Attention Score (AAS). Descriptive and correlation statistics were performed to study the characteristics of these articles in relation to their publication data, research type and domain, number of Mendeley readers, and dimensions citations. Citation counts were extracted from Scopus and Google Scholar. The median AAS for the top 100 outputs was 22 (range from 12 to 458). The outputs were mostly discussed on Twitter (median = 8; range = 0-131). Topics discussing treatment and care for patients with CL/P accounted for 38% of the articles with the highest AAS followed by etiology and risk factors (32%). The majority of articles originated from the USA (46%) followed by Europe (16%) and the United Kingdom (15%). No significant differences were observed in AAS among different study designs, topic domains, journals' ranking and impact factor, and the number of citations in Scopus and Google Scholar. Researchers should consider use of social platforms to disseminate their work among scholars and nonscholars. Altmetrics can be combined with traditional metrics for a more comprehensive assessment of research impact.