Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author’s identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, mo...

This study analyzes collaborative and networked academic authorship in higher education. The literature review shows evidence that single authorship has made a gradual paradigm shift to joint authorship. The empirical evidence from the Turku University of Applied Sciences indicates that collaborative authorship has notably increased in the last few years. Co-authorship has extended outside the ...

In this study, we propose and validate social networks based theoretical model for exploring scholars’ collaboration (co-authorship) network properties associated with their citation-based research performance (i.e., gindex). Using structural holes theory, we focus on how a scholar’s egocentric network properties of density, efficiency and constraint within the network associate with their scho...

Research on social processes in the production of scientific output suggests that the collective research agenda of a discipline is influenced by its structural features, such as "invisible colleges" or "groups of collaborators" as well as academic "stars" that are embedded in, or connect, these research groups. Based on an encompassing dataset that takes into account multiple publication types...

This study analyzes collaborative and networked academic authorship in higher education. The literature review shows evidence that single authorship has made a gradual paradigm shift to joint authorship. The empirical evidence from the Turku University of Applied Sciences indicates that collaborative authorship has notably increased in the last few years. Co-authorship has extended outside the ...

Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of ...

Health professionals publishing within the field of health sciences continue to experience issues concerning appropriate authorship, which have clinical, ethical, and academic implications. This integrative review sought to explore the key issues concerning authorship from a bioethical standpoint, aiming to explore the key features of the authorship debate. Studies were identified through an el...

This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, wh...

The study, based on the Web of Science, analyses 758 articles published from 2000 to 2014. Our analysis includes the publications’ output, authorship, institutional and country patterns of production, citations and collaboration. A Social Network Analysis was conducted to identify primary groups of researchers and institutions and the collaboration between countries.