The Association Between Professional Accounts on Social Networks Twitter and ResearchGate and the Number of Scientific Publications and Citations Among Anesthesia Researchers: Observational Study

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Abstract

Background

Social networks are now essential tools for promoting research and researchers. However, there is no study investigating the link between presence or not on professional social networks and scientific publication or citation for a given researcher.

Objective

The objective of this study was to study the link between professional presence on social networks and scientific publications/citations among anesthesia researchers.

Methods

We included all the French full professors and associate professors of anesthesia. We analyzed their presence on the social networks Twitter (professional account with ≥1 tweet over the 6 previous months) and ResearchGate. We extracted their bibliometric parameters for the 2016-2020 period via the Web of Science Core Collection (Clarivate Analytics) database in the Science Citation Index-Expanded index.

Results

A total of 162 researchers were analyzed; 42 (25.9%) had an active Twitter account and 110 (67.9%) a ResearchGate account. There was no difference between associate professors and full professors regarding active presence on Twitter (8/23 [35%] vs. 34/139 [24.5%], respectively; P=.31) or ResearchGate (15/23 [65%] vs. 95/139 [68.3%], respectively; P=.81). Researchers with an active Twitter account (median [IQR]) had more scientific publications (45 [28-61] vs. 26 [12-41]; P<.001), a higher h-index (12 [8-16] vs. 8 [5-11]; P<.001), a higher number of citations per publication (12.54 [9.65-21.8] vs. 10.63 [5.67-16.10]; P=.01), and a higher number of citations (563 [321-896] vs. 263 [105-484]; P<.001). Researchers with a ResearchGate account (median [IQR]) had more scientific publications (33 [17-47] vs. 26 [9-43]; P=.03) and a higher h-index (9 [6-13] vs. 8 [3-11]; P=.03). There was no difference between researchers with a ResearchGate account and those without it concerning the number of citations per publication and overall number of citations. In multivariate analysis including sex, academic status, and presence on social networks, the presence on Twitter was associated with the number of publications (β=20.2; P<.001), the number of citations (β=494.5; P<.001), and the h-index (β=4.5; P<.001).

Conclusions

Among French anesthesia researchers, an active presence on Twitter is associated with higher scientific publication and citations.

Related collections

Most cited references 24

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Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

Gunther Eysenbach (2011)

Background Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. Objective (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. Methods Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. Results A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4–33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. Conclusions Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.

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Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

Stefanie Haustein, Rodrigo Costas, Vincent Larivière (2015)

A number of new metrics based on social media platforms—grouped under the term “altmetrics”—have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters’ Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators.

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Do Altmetrics Work? Twitter and Ten Other Social Web Services

Mike Thelwall, Stefanie Haustein, Vincent Larivière … (2013)

Altmetric measurements derived from the social web are increasingly advocated and used as early indicators of article impact and usefulness. Nevertheless, there is a lack of systematic scientific evidence that altmetrics are valid proxies of either impact or utility although a few case studies have reported medium correlations between specific altmetrics and citation rates for individual journals or fields. To fill this gap, this study compares 11 altmetrics with Web of Science citations for 76 to 208,739 PubMed articles with at least one altmetric mention in each case and up to 1,891 journals per metric. It also introduces a simple sign test to overcome biases caused by different citation and usage windows. Statistically significant associations were found between higher metric scores and higher citations for articles with positive altmetric scores in all cases with sufficient evidence (Twitter, Facebook wall posts, research highlights, blogs, mainstream media and forums) except perhaps for Google+ posts. Evidence was insufficient for LinkedIn, Pinterest, question and answer sites, and Reddit, and no conclusions should be drawn about articles with zero altmetric scores or the strength of any correlation between altmetrics and citations. Nevertheless, comparisons between citations and metric values for articles published at different times, even within the same year, can remove or reverse this association and so publishers and scientometricians should consider the effect of time when using altmetrics to rank articles. Finally, the coverage of all the altmetrics except for Twitter seems to be low and so it is not clear if they are prevalent enough to be useful in practice.

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Author and article information

Contributors

Thomas Clavier:

ORCID: https://orcid.org/0000-0001-8630-7537

Department of Anesthesiology, Critical Care and Perioperative MedicineRouen University Hospital1 rue de GermontRouen, 76000France33 232881733thomasclavier76@gmail.com

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: October 2021

Publication date (Electronic): 15 October 2021

Volume: 23

Issue: 10

Electronic Location Identifier: e29809

Affiliations

[1 ] Department of Anesthesiology, Critical Care and Perioperative Medicine Rouen University Hospital Rouen France

Author notes

Corresponding Author: Thomas Clavier thomasclavier76@ 123456gmail.com

Author information

Thomas Clavier https://orcid.org/0000-0001-8630-7537

Emilie Occhiali https://orcid.org/0000-0003-2364-8662

Zoé Demailly https://orcid.org/0000-0001-9768-0193

Vincent Compère https://orcid.org/0000-0001-5977-7090

Benoit Veber https://orcid.org/0000-0002-5938-7284

Jean Selim https://orcid.org/0000-0002-1395-1601

Emmanuel Besnier https://orcid.org/0000-0002-0267-0986

Article

Publisher ID: v23i10e29809

DOI: 10.2196/29809

PMC ID: 8556638

PubMed ID: 34652279

SO-VID: 51e6c0aa-5ff6-4183-af45-1397eeced04f

Copyright © ©Thomas Clavier, Emilie Occhiali, Zoé Demailly, Vincent Compère, Benoit Veber, Jean Selim, Emmanuel Besnier. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.10.2021.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 21 April 2021

Date revision requested : 10 June 2021

Date revision received : 22 July 2021

Date accepted : 12 August 2021

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