Making visible the invisible through the analysis of acknowledgements in the humanities

A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains

Analysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher’s collaboration patterns, context in which research was conducted, and specific aspects of the academic work. The focus of the present research is the analysis of a large sample of acknowledgement texts indexed in the Web of Science (WoS) Core Collection. Record types “article” and “review” from four different scientific domains, namely social sciences, economics, oceanography and computer science, published from 2014 to 2019 in a scientific journal in English were considered. Six types of acknowledged entities, i.e., funding agency, grant number, individuals, university, corporation and miscellaneous, were extracted from the acknowledgement texts using a named entity recognition tagger and subsequently examined. A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete. The analysis of the automatically extracted entities revealed differences and distinct patterns in the distribution of acknowledged entities of different types between different scientific domains. A strong association was found between acknowledged entity and scientific domain, and acknowledged entity and entity type. Only negligible correlation was found between the number of citations and the number of acknowledged entities. Generally, the number of words in the acknowledgement texts positively correlates with the number of acknowledged funding organizations, universities, individuals and miscellaneous entities. At the same time, acknowledgement texts with the larger number of sentences have more acknowledged individuals and miscellaneous categories.

Dataset of identified scholars mentioned in acknowledgement statements

Acknowledgements represent scholars' relationships as part of the research contribution. While co-authors and citations are often provided as a well-formatted bibliometric database, acknowledged individuals are difficult to identify because they appear as part of the statements in the paper. We identify acknowledged scholars who appeared in papers published in open-access journals by referring to the co-author and citation relationships stored in the Microsoft Academic Graph (MAG). Therefore, the constructed dataset is compatible with MAG, which accelerates and expands the acknowledgements as a data source of scholarly relationships similar to collaboration and citation analysis. Moreover, the implemented code is publicly available; thus, it can be applied in other studies.

Estudio de la sección “agradecimientos” en una muestra de la Revista Española de Documentación Científica y de Scientometrics: evolución y autoridades

El presente trabajo analiza los agradecimientos hacia autoridades (personas e instituciones) incluidos en los artículos de dos revistas científicas, la Revista Española de Documentación Científica (REDC) y Scientometrics, para ver la evolución temporal que éstos han sufrido durante veiniún años en periodos alternos de cuatro años (1998, 2002, 2006, 2010, 2014 y 2018) y estudiar su relación con la autoría/citación científica, y con los patrones de financiación de la Ciencia. Se analizan 1354 artículos, 717 de los cuales contienen agradecimientos que proporcionan un total de 2127 menciones. Los principales resultados indican que, aunque existe mayor tradición por incluir agradecimientos en sus artículos en Scientometrics, la revista española alcanza una Tasa de Variación en 2018 respecto a 1998 mucho mayor, lo que señala una evolución muy positiva por parte de la revista española; también reflejan que la práctica común en ambas revistas consiste en mencionar en los agradecimientos más a las personas que han ayudado en la investigación, y no tanto a las instituciones; y, por último, que existe una correlación positiva moderada entre el número de apariciones de los autores en los agradecimientos y su Índice h, por lo que sería posible utilizar los agradecimientos para medir la actividad científica.

Examining influential factors for acknowledgements classification using supervised learning

Acknowledgements have been examined as important elements in measuring the contributions to and intellectual debts of a scientific publication. Unlike previous studies that were limited in the scope of analysis and manual examination. The present study aimed to conduct the automatic classification of acknowledgements on a large scale of data. To this end, we first created a training dataset for acknowledgements classification by sampling the acknowledgements sections from the entire PubMed Central database. Second, we adopted various supervised learning algorithms to examine which algorithm performed best in what condition. In addition, we observed the factors affecting classification performance. We investigated the effects of the following three main aspects: classification algorithms, categories, and text representations. The CNN+Doc2Vec algorithm achieved the highest performance of 93.58% accuracy in the original dataset and 87.93% in the converted dataset. The experimental results indicated that the characteristics of categories and sentence patterns influenced the performance of classification. Most of the classifiers performed better on the categories of financial, peer interactive communication, and technical support compared to other classes.

Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science

‘Social media metrics’ are bursting into science studies as emerging new measures of impact related to scholarly activities. However, their meaning and scope as scholarly metrics is still far from being grasped. This research seeks to shift focus from the consideration of social media metrics around science as mere indicators confined to the analysis of the use and visibility of publications on social media to their consideration as metrics of interaction and circulation of scientific knowledge across different communities of attention, and particularly as metrics that can also be used to characterize these communities. Although recent research efforts have proposed tentative typologies of social media users, no study has empirically examined the full range of Twitter user’s behavior within Twitter and disclosed the latent dimensions in which activity on Twitter around science can be classified. To do so, we draw on the overall activity of social media users on Twitter interacting with research objects collected from the Altmetic.com database. Data from over 1.3 million unique users, accounting for over 14 million tweets to scientific publications, is analyzed. Based on an exploratory and confirmatory factor analysis, four latent dimensions are identified: ‘Science Engagement’, ‘Social Media Capital’, ‘Social Media Activity’ and ‘Science Focus’. Evidence on the predominant type of users by each of the four dimensions is provided by means of VOSviewer term maps of Twitter profile descriptions. This research breaks new ground for the systematic analysis and characterization of social media users’ activity around science.