Introduction
Tissue Engineering (TE) is a multidisciplinary discipline aiming to develop biological substitutes that can restore, maintain, or even improve the structure or functionality of damaged tissues (Langer & Vacanti, 1993). Since its appearance in 1988 (Viola, Bal, & Grad, 2003), TE has globally spread to improve current therapeutic approaches entailing a revolution in health sciences (Kaul & Ventikos, 2015). In this sense, several TE devices have been employed in the treatment of damaged blood vessels (Kumar, Brewster, Caves, & Chaikof, 2011), peripheral nerve injuries (Carriel, Alaminos, Garzon, Campos, & Cornelissen, 2014), chronic skin ulcerations (Debels, Hamdi, Abberton, & Morrison, 2015) , oral mucosal replacement (Martín-Piedra et al., 2016; Sanchez-Quevedo et al., 2007) and corneal lesions (Rico-Sánchez et al., 2019).
This growing interest in TE research has been demonstrated through the increasing number of TE documents, such as the recent literature states (Santisteban-Espejo et al., 2018). Besides, in order to identify TE global trends and to define the cognitive and social framework that undergoes TE scientific evolution, several analyses have also been performed (Santisteban-Espejo et al., 2019). These bibliometric-based studies can help administrative authorities to better plan funding allocations and to promote synergies within TE scientific community, as previously stated in other scientific areas (Abramo, D’Angelo, & Caprasecca, 2009).
In this context, traditional bibliometric analysis has used the information extracted from academic documents (i.e. citations or keywords) in order to understand the development of TE area (Dai G, 2000; Santisteban-Espejo et al., 2019; Santisteban-Espejo et al., 2018). Nevertheless, these classical bibliometric methods have been criticized in recent years because their fewer adequacy to comprehend the real online attention of scientific research. As a consequence, alternative metrics, formerly called Altmetrics, have been developed to evaluate scientific behaviour through information content at social media (Priem, Piwowar, & Hemminger, 2011).
This altmetric methodology describes a web-based metric for the impact of publications and other scholarly material by using data from social media platforms (i.e.Twitter, Facebook, Google+, blogs, Mendeley, CiteULike, Reddit and Wikipedia, among others) (Veeranjaneyulu, 2017). The appearance of this type of measures is related to the social media revolution; there are now different groups of the population, non-author professionals, which read research articles and now also share them and, furthermore, new types of academic outputs have appeared (Moral-Munoz & Cobo, 2018).
Consequently, the traditional acceptance that the research output only was disseminated within the scientific community has now changed. In addition, the online public nature of these alternative tools permits to track mentions of scholarly articles across the online landscape faster and broader than traditional citation metrics (Verma, 2018). The impact of the Altmetrics and its complementary role in association with traditional bibliometrics have been well stated in several disciplines (Haustein, Bowman, & Costas, 2016).
In this context of global science, where information is shared in the social web, even previously to its communication within the academic community, it would be interesting to analyse the real scientific impact of TE in our society. To our knowledge, no documents are currently available evaluating this aspect in TE research field. Therefore, the primary aim of this study is to determine the online dimension of TE scientific production in social media and to correlate it with traditional scholarly impact. Then, we perform a factorial analysis to identify the components that could explain the correlation results and, finally, we developed a prediction equation for future TE citations based on altmetrics data.