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Increased Citations Open Access Social Media

Written By Sunday, May 1, 2022 Add Comment Edit

Accepted on 10 Jul 2020Submitted on 15 Jun 2020

Introduction

The move towards providing open access to inquiry outputs was initiated with the Budapest Open Access (OA) Initiative in 2002 ( Chan et al. 2002 ; Piwowar et al. 2018 ) and it has been widely accepted as a desirable phenomenon and has go a reality in many bookish spheres ( Kriegeskorte et al. 2012 ). OA publications are mainly divided into Golden and Green OA. Publications published in a full open up access journal are called Gold OA. Greenish publications are those where a preprint version of the article is made available in, for example, an institutional repository. This could possibly happen after a menstruation of embargo ready by the periodical that publishes the article ( Holmberg et al. 2020 ). In add-on to the broad categories of Aureate and Dark-green OA, there are also other types of OA such equally Hybrid and Bronze. In the hybrid OA model, publishers publish OA articles in closed-access scholarly journals, after authors have paid article processing charge (APC) ( Kanjilal & Das 2015 ). Bronze publications are those manufactures fabricated available freely to read on the publisher's website, without an explicit Open license ( Piwowar et al. 2018 ).

This study aims to compare the social media visibility of Open Access (OA) versus non-Open Access (non-OA), and Gold type of OA versus other types of OA articles in Life Sciences and Biomedicine. The visibility of OA vs non-OA articles, in terms of citations, has been widely investigated. Whilst some studies have suggested a causal relationship between commodity OA status and higher citation counts ( Eysenbach 2006 ; Hajjem et al. 2006 ; Gargouri et al. 2010 ), some subsequently studies identified weaknesses in the methodology used in those studies where a citation reward was constitute ( McCabe and Snyder 2015 ; Hersh and Plume 2016 ; Hua et al. 2016 ). OA articles take also shown to have a social media visibility advantage ( McKiernan et al. 2016 ). A study of over 2,000 articles published in Nature Communications showed that manufactures published openly received nearly double the number of unique tweeters and Mendeley readers than subscription-based manufactures ( Adie 2014 ). The results of a similar study on 1,761 Nature Communications articles showed that OA manufactures received 1.2–1.48 times as much social media attention (Twitter and Facebook) equally compared to non-OA articles ( Wang et al. 2015 ). In the area of Information Scientific discipline, Cintra, Furnival and Milanez ( 2018 ) establish that OA articles in hybrid journals received more than citations, Mendeley readers and Altmetrics attention score, when compared to closed articles in the same journals. In a study of national publications from Finnish universities (2012–2014), Holmberg et al. ( 2020 ) investigated how ofttimes articles published in national OA journals (Gold journals) received social media attending and citation in comparison to articles in subscription-based journals. The results showed significant disciplinary and platform differences in terms of OA advantage. For instance, OA articles in Veterinary Sciences, Social & Economic Geography and Psychology received more citations and social media attention, while the reverse was found for manufactures inside Medicine and Wellness Sciences.

Few other studies have shed light on factors that affect the social visibility of scientific outputs ( Haustein et al. 2015 ; Andersen & Haustein 2015 ; Didegah, Bowman & Holmberg 2018 ). These common factors include the characteristics of articles such as journal impact factor, international collaboration, individual collaboration, funding, abstract readability and abstract title ( Didegah, Bowman & Holmberg 2018 ). The results show that important factors vary beyond different social media platforms. While institution prestige and land prestige are associated with increased Mendeley readers, web log and news posts, they are insignificant factors for Twitter and Facebook posts ( Didegah, Bowman & Holmberg 2018 ).

Alhoori et al. ( 2015 ) studied the impact of OA status of articles on their visibility, whilst decision-making for the effects of journal and publication year. The results of the written report showed that OA articles received more altmetric counts than not-OA articles on eight online platforms. However, when controlling for journal and publication year, no clear relationship was found between OA and altmetric mentions. In a written report of a random sample of one,000,000 articles published, Fraser et al. ( 2019 ) compared the altmetric counts of OA articles categorized in four groups (Gold, Green, Hybrid and Bronze) in comparing to not-OA articles. Their findings showed that the highest altmetric counts were for Statuary and Dark-green OA, and the lowest altmetric counts were for non-OA. Nevertheless, taking factors such equally subject nomenclature and state of authorship into business relationship, the results changed; for example, in Humanities, Gilded OA articles were the least tweeted.

Our study follows in the same vein and aims to determine whether, and to what extent, the OA status and OA type (Gold vs. not-Gold) of an article predicts their social media visibility. However, this written report extends this line of research by simultaneously controlling for a combination of factors that according to the previous literature were institute to be significantly influencing both citation and altmetric counts of inquiry manufactures. This would help to ensure that the regression model more than precisely determines the extent to which OA publishing might influence the online visibility of research articles. Furthermore, many of the factors included in our regression analysis have not been studied in the previous enquiry. These factors include journal impact, mega journals, dissimilar types of collaboration, MESH category, multidisciplinarity, research funding, lay summaries, F1000 score and authors' gender. In this paper, we were especially interested in comparing Gold OA to other types of OA (Green, Bronze and Hybrid). The reason for this is Program S. The Plan S initiative advocates a time to come where all papers are open up access and is based on the view that "free admission to all scientific publications from publicly funded research is a moral right of citizens" ( European Commission 2019 ). The goal of the program is to decrease the gap between the rich and poor, simply the results of some studies advise that this may exist hard due to the APC charge per unit for Aureate OA ( Van Leeuwen, Robinson-Garcia & Costas 2019 ).

To accost the research objectives, researchers formulated the following questions:

  1. To what extent does the OA condition (OA, not-OA) of manufactures predict their social media visibility, when controlling for a number of of import factors (journal bear upon, individual collaboration, research funding, number of MESH topics, international collaboration, lay summary, F1000 Score, mega journal, MESH topic, F1000 Score, and the gender of first and last authors)?
  2. To what extent does the OA type (Gold vs. non-Gold) of articles affect their social media visibility, when controlling for a number of important factors (journal impact, individual collaboration, inquiry funding, number of MESH topics, international collaboration, lay summary, F1000 Score, mega periodical, MESH topic, F1000 Score, and the gender of first and last authors)?

Methodology

Data collection

The data for this study comprised 83,444 articles and reviews in the research area of Life Sciences & Biomedicine from 2012–2016, retrieved from the Web of Scientific discipline Medline in November 2018 using this query: SU = Life Sciences & Biomedicine. Using articles' PMID, a search was conducted in Altmetric.com (October 2017 version) in order to obtain the following altmetric indicators: tweet counts, Facebook posts, news posts, blog posts, F1000 post counts, F1000 score, policy mentions, and Wikipedia mentions.

Dependent, independent and control variables

The numbers of tweets, Facebook posts, news posts, and blog posts were considered as dependent variables in a regression model (explained below) to measure the extent to which the OA condition of an article may predict its social media visibility. OA status of the articles and their OA types were obtained from Unpaywall.org in Nov 2019. In the same model, the OA status of articles and OA types (gold vs. not-gold OA) were considered as the independent variables and several other variables equally control variables or covariates.

The association between OA factors and social media counts may vary past calculation or removing controlled factors from the regression model ( Fraser et al. 2019 ). Hence, a number of control variables that may interfere the association were identified and entered into the regression model simultaneously. While previous inquiry has introduced several factors in association with citation counts, not all potential factors are examined in association with altmetric counts. Given the high correlation found between citation and altmetric counts ( Thelwall et al. 2013 ), same citation factors could be likewise important for social media visibility. Hence, the variables taken into account in this report are the important factors that significantly influenced both commendation and social media counts of inquiry articles in previous studies. Journal bear upon (gauged past SNIP and mega journals in this study) and research collaboration were the most important factors associating with both citation and altmetric counts in difference subject area fields ( Didegah & Thelwall 2013 ; Didegah, Bowman & Holmberg 2018 ). Research funding was establish to be an important factor for commendation counts in Life Sciences and Medicine ( Didegah 2014 ). Both commendation and altmetric counts were besides plant to vary across subject area fields ( Didegah & Thelwall 2013 ; Didegah, Bowman & Holmberg 2018 ). Because this study is done in the area of Life Sciences and Biomedicine, MESH categories were an advisable subject field classification to consider. Few factors are rather new in the field such as lay summaries, author genders and F1000 score. Lay summaries are a potential factor initially proposed by Didegah, Alperin & Haustein ( 2018 ) but its clan with altmetric counts is yet a matter of question. Author genders also found to be an important factor on some altmetric platforms ( Sotudeh, Dehdarirad, & Freer 2018 ).

Table i lists all these unlike variables and their descriptions; few variables and how they were measured are further explained below. We as well used publication year as an offset variable in the regression model. one

Table ane

Dependent variables, independent variables and covariates for the hurdle model.

To determine the gender of the authors (first and last), nosotros used Gender API (https://gender-api.com/). This service offers a standard first name search with the possibility to handle double names. The response contains gender assignments (male, female person, or unknown), plus confidence parameters, samples and accuracy ( Santamaría and Mihaljević, 2018 ). In cases of gender-neutral, unknown, initials or in cases where the accuracy was lower than 80%, the names were checked manually using cyberspace searches and authors' websites. The gender of 35 authors were remained unidentified. In our regression model they were regarded as missing values. 2

Regarding mega journals as a covariate in the regression model, we used the journal list provided by Spezi et al.'south ( 2017 ) study to decide whether a journal was mega periodical or not.

Medline assigns articles to 14 broad MESH. In this article, only 7 categories including Anatomy, Organisms, Diseases, Chemicals and Drugs, Belittling, Diagnostic and Therapeutic Techniques and Equipment, Psychiatry and Psychology and Health Care as the most relevant medical topics were considered for evaluation. For each of the 7 MESH categories, we created a dummy variable every bit a control variable in the regression models.

F1000 score every bit an altmetric indicator is included in the model every bit a control variable. The rationale behind this is that manufactures scored in F1000 are recommended as highly of import works in the fields of life sciences, health and concrete sciences and beyond ( Faculty Opinions 2020 ). Thus, this factor may bear on the online visibility of articles in the field, regardless of their open access status.

Data analysis and procedures

Hurdle model

Descriptive statistics was used to depict the state-of-the-art of OA manufactures vs. non-OA articles shared on the seven altmetrics platforms. Two-sample proportion tests were likewise performed in club to compare the proportion of OA papers shared on different altmetrics platforms in comparing to not-OA articles.

To answer the second research question, given that the dependent variables (altmetric counts) of this report were count information, count regression models were used. Every bit altmetric counts are over-dispersed and include excessive number of zeros, a count model is required to deal with these two problems.

Starting time, a standard negative binomial, a zippo-inflated negative binomial and a hurdle negative binomial models were applied. A standard model is oft used to model overdispersed data. Zilch-inflated models are used for overdispersed and excessive zero datasets and assume that in that location are two types of zeros in the information: zeros which arise from a negative binomial count distribution and zeros which arise from a "perfect-null" distribution ( Hilbe, 2011 ). Hurdle models measure the likelihood of an ascertainment being positive or zero, and and then determine the parameters of the count distribution for positive observations. Thus, a hurdle model comprises two parts: the count model, which is a negative binomial model, and the logit model. The count model predicts changes in non-zero social media counts, whilst the logit model reports the changes in nothing social media mentions for a unit of measurement change in the open up access factors and each of the covariates.

We finally concluded that a negative binomial-logit hurdle model was the all-time fit for the data as it creates a scenario in which the positive counts follow a Poisson or NB distribution after passing a hurdle to gain positive counts ( Didegah, Bowman, & Holmberg 2018 ).

Therefore, in club to measure the extent to which the OA type (Gold OA vs Green, Bronze, and Hybrid) of an commodity may affect its social media visibility, researchers ran a a hurdle model to appraise the association betwixt OA type of articles and the number of times they were mentioned on each altmetrics platform. The OA type was considered equally a binary variable (Golden vs. Non-Gold), because of the small amount of publications that were Green or Hybrid. Moreover, the distribution of Statuary articles on Facebook, news and blogs was too pocket-size to be statistically reliable.

Just Twitter, Facebook, news and blogs were considered for the second and tertiary research questions, as there were a pregnant number of manufactures visible on these platforms (See Figure 1). To obtain the most reliable results, a number of important factors that possibly had an affect on social media visibility of an article were entered into the model as control variables (Run across Table one).

Figure 1

The pct of OA vs Not-OA manufactures in Life Sciences and Biomedicine (2012–2016) across dissimilar social media platforms.

Multicollinearity

Multicollinearity happens when ii contained variables/covariates are highly correlated. It is troublesome in regression models because it affects the relationship between the predictors and the output variable ( Didegah 2014 ). A pop examination to diagnose multicollinearity is the Variance Aggrandizement Cistron (VIF). The VIF is based on the proportion of variance that a predictor variable shares with other predictors in the model. There are several rules of thumb of 4, 10, 20, and over, based on which VIFs over 4, 10, 20, or more are considered to show a high multicollinearity.

The VIF is tested for both the OA status and OA type factors in correlation with the covariates and the results are reported in Table 2. Equally can be seen, the VIFs of both groups of variables are rather low and they do not exceed any standards. That said, this problem will not bear upon the results of the regression models.

Table two

VIF results for both the OA type and OA status factors.

Results

From a total of 83,444 papers, 49,598 (59.44%) were OA and 30,137 (36.12%) were not-OA. 3709 (4.44%) papers did not have any OA status, co-ordinate to Unpaywall.org. Twitter had the highest percent of OA articles (35.45%), followed by Facebook (8.95%), blogs (5.xiii%), news (4.60%), Wikipedia (1.82%), F1000 (1.79%) and policy documents (0.57%), respectively. (See Figure ane). The highest percentage of not-OA articles were also shared on Twitter (thirteen.36%), followed by Facebook (2.44%), blogs (1.11%), and news (1.10%).

The results of the two proportion tests besides showed that the percentage of OA articles mentioned on altmetrics platforms was significantly higher than that of the non-OA manufactures [P < 0.0001].

Figure two shows the percentage of each type of OA. Every bit can be seen from the effigy, Gilded OA had the highest per centum of articles being followed by Bronze and Green OA types in the 2nd and 3rd place, respectively.

Figure 2

The percent of articles in Life Sciences and Biomedicine (2012–2016) across different OA types.

The detailed results of hurdle models for each platform are as follows.

Twitter

Regarding the count model, both the OA vs. non-OA (hereafter OA status) and Gilt vs. non-Gilt (hereafter OA type) factors were significantly associated with an increment in the estimated number of received tweets. While a unit change in the OA status increased the estimated number of received tweets by 92.7%, the OA type contributed to 28.4% increase in the estimated received tweet counts. Amongst the types of OA, Gold OA is found to be a more of import factor compared to other types of OA. By a unit change in the cistron, which means moving from other types of OA together (Green, Bronze, and Hybrid) to Gilt OA, the number of tweets to the article on average volition approximately increment past 28.4%. The logit model also confirmed that OA status and OA type of manufactures were significantly associated with the increased probability of a paper beingness mentioned in Twitter.

As for the controlled factors in both OA status and OA type models, the count model showed that the periodical impact (measured by SNIP), mega journal, individual collaboration, international collaboration, lay summary, F1000 score, and being indexed under 'Psychiatry and Psychology' MESH category significantly contributed to higher number of received tweets, on average. Neither the gender of last writer nor the number of mesh categories was found to be an of import factor in the OA condition and OA types count models (Table 3). Furthermore, as demonstrated by the count and logit models for OA condition, Funding had a weak negative association with the average number of tweets counts as well as the probability of being mentioned on Twitter.

Tabular array three

Hurdle model results for Twitter.

ane Change in the hateful parameter of positive citation counts for a unit increase.

2 Change in the probability of aught citations for a unit increase.

iii Sig. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.ane '' 1.

Facebook

As can be seen from Tabular array 4, open access articles had a higher average number of Facebook mail counts, when controlling for a number of important factors. A unit change in the OA status (changing from not-OA status to the OA status) approximately increased the average number of Facebook posts by 25.seven%. This was the weakest association, when compared with the 3 other social media platforms. According to the logit models, whilst the open access status significantly associated with a college probability of being mentioned in Facebook, the open up admission type did not.

Table 4

Hurdle model results for Facebook.

Regarding the controlled factors in count models, international collaboration, the number of MESH topics, F1000 score, and Periodical bear on (SNIP) were found to be associated with a higher number of received Facebook posts.

Past looking at the count models for both OA status and OA types, it tin can be concluded that funding and 'Anatomy' MESH category were institute to be negatively associated with the estimated number of received Facebook posts. Finally, the gender of starting time and last authors were significant factors for the higher number of received Facebook posts in the OA status model (Table iv). Even so, they were not significant factors for the estimated number of received Facebook posts in the OA type models.

News

As tin can exist seen from both count and logit models for the OA condition, for news outlets, the OA status of articles was a meaning factor for both a higher probability of a paper being mentioned in news and a college boilerplate number of received news mentions (Run across Tabular array 5). Past increase of a unit of measurement in the OA factor (changing from not-OA status to OA status), the estimated number of News mentions to articles increased by 83.nine%. This association was college than the association between the received news mentions and international collaboration, journal impact and other significant factors in the model.

Table 5

Hurdle model results for News.

However, the OA type was not a pregnant contributing cistron for the estimated number of news mentions or for the probability of existence mentioned in news outlets.

For both OA condition and OA type models, international collaboration, journal impact (SNIP), number of MESH topics and F1000 score significantly contributed to higher odds of visibility for the articles on News platforms.

Regarding the types of MESH categories, our findings from both logit and count models (OA vs non-OA) showed that articles indexed under specific MESH topics ('Chemicals and Drugs' and 'Analytical, Diagnostic and Therapeutic Techniques') had a news disadvantage in terms of odds of existence mentioned in news outlets and the estimated number of received news mentions compared to other MESH topics. Furthermore, the results of logit model for both (OA vs non-OA; Golden vs not-Gold), showed that articles indexed nether 'Psychiatry and Psychology' were significantly more than likely to exist mentioned in news outlets.

The gender of first and last authors, lay summary and funding were not meaning factors for the estimated number of News mentions received in both OA status and OA blazon models. However, the gender of first author was significantly associated with a higher probability of existence mentioned in news outlets. Interestingly, Funding was significantly associated with lower odds of being mentioned in news outlets in OA status model.

Blogs

The results of count regression models (Encounter Tabular array six) showed that OA articles on average had significantly a higher number of blog mentions. Yet, the OA type of articles was not a significant factor for this platform. A unit change in the OA status increased the estimated number of blogs mentions for manufactures by 48.4%. The logit models showed that both the OA status and OA blazon significantly associated with college probability for a paper to be mentioned in blogs.

Table half-dozen

Hurdle model results for blogs.

As for the controlled factors, international collaboration, journal impact (SNIP), mega journals, number of MESH topics, lay summary and F1000 score significantly contributed to a college probability for manufactures to be mentioned in blogs, both when they were examined together with the OA status and OA type factors. Interestingly, the clan of lay summary with blog mentions was highly pregnant for both OA condition and OA blazon regression models (both count and logit models). In the OA status model, a unit change in the lay summary (that is changing from not having a lay abstruse in the article to having a lay abstract) may increase the number of blogs mentions for articles past 37.eight% which was higher than the contribution of the international collaboration (xiv.nine%) and journal impact (29.6%).

Regarding MESH topics, the results from both logit and count models (OA vs non-OA; Aureate-vs non-Golden) showed that specific MESH topics ('Beefcake', 'Diseases' and 'Chemical and drugs') had a disadvantage in terms of odds of beingness mentioned in blogs, besides every bit the estimated number of received blog mentions. The exception to these was the 'Psychiatry and Psychology' MESH category. According to the count and logit models results for OA condition, the articles indexed under this MESH category were 65.4% more probable to be mentioned by blogs and to receive threescore.three% more blog mentions than the residuum of the articles. Furthermore, according to the logit model for OA blazon, manufactures that were categorized nether the same MESH group, were likewise 78.1% more than probable to be mentioned by blogs. The gender of concluding authors and funding were not meaning factors for the odds of being mentioned in blogs or the estimated number of received blog mentions. Still, the gender of first author was weakly associated with the lower odds of being mentioned in blogs for both OA status and OA type models.

Discussion and conclusion

This report aimed to determine whether, and to what extent, the OA condition (whether an commodity is open or closed) and the OA blazon (whether an article is Gilded or non-Gold) of an article in Life Sciences and Biomedicine tin can predict its social media visibility, when controlling for a number of important factors. These factors were individual collaboration, research funding, number of MESH topics, topic, gender of first and terminal authors, beingness a mega journal, international collaboration, lay summary, and F1000 Score.

The findings of our study revealed that the pct of Life Sciences and Biomedicine OA articles (around 57%) mentioned on social media platforms was significantly higher than that of non-OA articles (around 36%).

Regarding the OA condition, our findings showed that beingness open was significantly associated with a higher probability of a paper being mentioned on the social media platforms studied. Furthermore, open up access articles had a higher average number of mentions on the studied platforms. This may provide insight for the European Commission every bit to whether they should include altmetric indicators as potential metrics for monitoring open up science advancement. The highest clan between OA status and the estimated number of received mentions was for Twitter (with a likelihood of 92.7% increment in the average number of tweets), whilst the everyman association was for Facebook (with a likelihood of 25.seven% increase in the average of Facebook posts). The former finding is in accordance with a big-scale study by Fraser et al. ( 2019 ) who found that manufactures in Medical and Health Sciences received more tweets overall. This shows the importance of making an commodity open, regardless of type, as this makes it easier for Twitter users to access the full text of articles.

Regarding OA types (studied every bit Gold vs non-Golden), our findings showed that although Golden OA was the nigh common OA type in our studied sample, Gold OA was just associated with a higher average of Tweets counts received and a higher probability of a newspaper being mentioned in Tweets and blogs. While Programme Due south (and in that Aureate OA publishing) is meant to broaden the access and subsequently increase visibility, our findings seem to suggest that social media visibility has not yet been fully reached via Gold OA in the area of Life Sciences and Biomedicine. Our findings about Twitter may be due to the fact that Twitter is a existent-time microblog network. Consequently, an article might be tweeted within few hours after publication ( Yu et al. 2017 ). Furthermore, Gilded OA publications might be immediately available through preprint repositories, before the official release. This is because policies regarding deposit location, license, and embargo requirements of Gold OA might exist less restrictive in comparison to other types of OA. Embargos on Green OA is an example of an access barrier ( Laakso 2014 ). Our findings contradicted that of Holmberg et al. ( 2020 ), who found that Gold OA publications had no Twitter advantage for Finnish publications within Medicine and Wellness Sciences. The departure may result from the different samples studied, especially as Holmberg et al. ( 2020 ) investigated a sample of Finnish articles indexed in a national database.

With regard to the covariates for the OA condition and OA type models, the results showed that overall, some covariates, such as international collaboration, journal impact and F1000 score were significantly associated with the college probability of beingness mentioned on the studied platforms, as well as the estimated number of received tweets, Facebook posts, news posts and weblog posts. Additionally, the 'Psychiatry and Psychology' MESH category topic was significantly associated with higher odds of visibility for both OA and Gold OA articles in all social media platforms studied. This finding is in line with Holmberg'southward et al. ( 2020 ) written report which plant that articles in the field of psychology had a clear OA advantage on Twitter. Our findings regarding news visibility of this MESH topic is in line with Kousha and Thelwall'south ( 2019 ) written report, which found that psychology and psychiatry were among the most frequently cited subject categories in U.k. newspapers. Ane reason for our finding, as suggested by Kousha and Thelwall ( 2019 ), might exist that news outlets prefer to report inquiry findings such every bit public or mental wellness issues that have more than full general involvement or benefit to the public. Furthermore, the less restricted access to OA manufactures in comparing with non-OA articles may arrive easier and faster for them to exist shared and disseminated.

Our findings too showed that some factors had a college association, an OA (dis) advantage, or were significant only on certain platforms. Some main examples of these findings are equally follows. Lay summary had a high clan with the number of received blog mentions (37.viii%). In 2013, it was stipulated that all the National Constitute for Health Research (NIHR) funded projects required lay summaries. The aim was to ensure that the results of scientific data is disseminated in an accessible and understandable style to the general public ( Kirkpatrick, et al. 2017 ). This is interesting, as with an increment in open access to biomedical research, some scientists and enquiry organizations might be more likely to blog about their enquiry. As another example, our findings showed that while the gender (female person) of beginning author was weakly associated with college odds of visibility for both OA and gilded OA articles in news outlets, information technology had a weak negative association with the odds of visibility in blogs. Our findings regarding blogs is in line with Paul-Hus' et al. ( 2015 ), who establish that overall, male first-authored papers had a slightly college mean number of blogs mentions in different studied disciplines. Our findings regarding news are in contrast with Sotudeh, Dehdarirad, and Freer ( 2018 ), who found no difference betwixt female and male first-authored papers in the field of neurosurgery in terms of visibility in news.

Regarding research funding as some other example, our findings for OA status models showed that funding had a weak negative association with i) the odds of a paper being mentioned in news outlets and on Twitter ii) the average number of tweets and Facebook post counts. The afterwards finding is in contrast with Didegah et al. ( 2018 ) which constitute that funding had a positive clan with average number of tweets and Facebook counts received. Notwithstanding, our findings regarding Twitter are in line with Álvarez-Bornstein and Costas ( 2018 ) which institute that biology was amongst the discipline categories with higher funding rates and lower proportion of Twitter mentions.

Collectively, this study provides initial exploratory findings regarding the association betwixt OA (status, type) and social media visibility, where decision-making for several important factors. The extent to which the OA factor associates with social media counts may vary by adding or removing factors from the models. However, the electric current model attempted to control for several important factors. By doing so, we were able to increase the probability of obtaining a more precise and reliable association between the OA factors and the average number of received social media mentions. It is of import to consider that our assay was limited to a sample of information in the area of Life sciences and Biomedicine. Thus, the results obtained in this article are non comprehensive and readers should do caution with generalization of the results beyond the example studied.

Notes

ane By otherwise nosotros mean the other thirteen MESH categories.

Competing Interests

The authors have no competing interests to declare.

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