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The main objective of this paper is to study the development and growth of scientific literature on women in science and higher education. A total of 1415 articles and reviews published between 1991 and 2012 were extracted from the Thomson Reuters Web of Science database. Standard bibliometric indicators and laws (e.g. Price's, Lotka's, and Bradford's laws) were applied to these data. In addition, the Gender Inequality Index (GII) was obtained for each country in order to rank them. The results suggest an upward trend not only in the number of papers but also in the number of authors per paper. However, this increase in the number of authors was not accompanied by greater international collaboration. The interest in gender differences in science extends too many authors (n = 3064), countries (n = 67), and research areas (n = 86). Data showed a high dispersion of the literature and a small set of core journals focused on the topic. Regarding the research areas, the area with the highest frequency of papers was Education and Educational Research. Finally, our results showed that countries with higher levels of inequality (higher GII values) tend to present higher relative values of scientific productivity in the field.

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This study aimed to determine whether, and to what extent, the OA status and OA type of articles can predict their social media visibility, when controlling for a considerable number of important factors. Those factors, which previous research confirmed their positive association with altmetric counts, were journal impact, individual collaboration, research funding, number of MESH topics, topic, international collaboration, lay summary, being a mega journal, F1000 Score, and gender of first and last authors. The data for this study comprised 83,444 articles and reviews in the research area of Life Sciences and Biomedicine from 2012-2016, retrieved from Medline in November 2018. The results showed that the percentage of OA articles mentioned on altmetric platforms was significantly higher than those of the non-OA articles. Furthermore, Open Access was significantly associated with a higher probability of a paper being mentioned on the studied social media platforms. Compared to non-OA articles, the OA articles had a higher average of tweets, Facebooks posts, news posts, and blog posts. By increase of a unit in the OA status, the average number of tweets, Facebooks posts, news posts, and blog posts increased by 92.7%, 25.7%, 83.9% and 48.4%, respectively. Regarding the OA types (studied as Gold vs non-Gold), our findings showed that the Gold OA articles had a higher average number of Tweets and a higher probability of being mentioned in tweets and blogs.

In this study, it was investigated whether early tweets counts could differentially benefit female and male (first, last) authors in terms of the later citation counts received. The data for this study comprised 47,961 articles in the research area of Life Sciences & Biomedicine from 2014–2016, retrieved from Web of Science’s Medline . For each article, the number of received citations per year was downloaded from WOS, while the number of received tweets per year was obtained from PlumX. Using the hurdle regression model, I compared the number of received citations by female and male (first, last) authored papers and then I investigated whether early tweet counts could predict the later citation counts received by female and male (first, last) authored papers. In the regression models, I controlled for several important factors that were investigated in previous research in relation to citation counts, gender or Altmetrics. These included journal impact (SNIP), number of authors, open access, research funding, topic of an article, international collaboration, lay summary, F1000 Score and mega journal. The findings showed that the percentage of papers with male authors in first or last authorship positions was higher than that for female authors. However, female first and last-authored papers had a small but significant citation advantage of 4.7% and 5.5% compared to male-authored papers. The findings also showed that irrespective of whether the factors were included in regression models or not, early tweet counts had a weak positive and significant association with the later citations counts (3.3%) and the probability of a paper being cited (21.1%). Regarding gender, the findings showed that when all variables were controlled, female (first, last) authored papers had a small citation advantage of 3.7% and 4.2% in comparison to the male authored papers for the same number of tweets.

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