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Researchers in academia and industry face a deluge of data in our digital world. In this paper, we investigate a novel problem, query-centric scientific topic evolution. Using heterogeneous graph mining techniques we construct a topic evolution tree (TET) from massive collections of scientific publications, enabling students and researchers to explore the foundation of research topics outside their specialization. Prior research has focused mainly on citation relationships; in this study we employed multiple types of relationships, including authorship, citation, publishing venue, and the contributions authors, papers, and venues have made to a specific topic. We examine multiple restricted meta-paths in constructing a TET covering topics from the MeSH vocabulary for biomedical research. [ABSTRACT FROM AUTHOR]

The growth of digital curation as a field of research has exploded in recent years. However, little work has been directed at the study of changes in the field, and no effort has been made to identify predominant trends and gaps in the research literature. This study examines the International Journal of Digital Curation (IJDC) over an eight-year period from its inception in 2006 to 2013. A total of 219 papers published in the IJDC was examined in terms of paper productivity, keywords in abstracts, authorship, citations received, and funding received. The preliminary results of this study found a steady upward trend in paper productivity. An overwhelming number of papers were coauthored papers from the United Kingdom and United States. The majority of the papers were cited more than once. Half the papers were supported by grants from federal government agencies. [ABSTRACT FROM AUTHOR]

Recently, the scholarly community has been eagerly exploring how AI‐produced content should be integrated into both academic writing and scholarly publishing. This paper investigates the prevailing responses to the introduction of ChatGPT in November 2022 and the interest that has been afforded it by both the academy and the publishing industry. A review of the published literature on aspects of ChatGPT authorship was carried out, finding that government and the publishing industry have unequivocally asserted that large language models (LLMs) like ChatGPT do not possess the traits of a person and are not able to author texts as a result. Other approaches, including practice, have been less vehement. To assess the integration of instructions on referencing ChatGPT using APA, top Google hits in the.edu domain were collected and analyzed over a 6‐week period from March 14 to April 18, 2023, a time during which official recommendations of the APA Style were finalized. Findings reveal that librarians were quick to provide guidance, but slow to update that guidance, contributing to the potential for misunderstanding the affordances of and best practices for work with LLMs. [ABSTRACT FROM AUTHOR]

With the countless advantages gained from the free, open, and ubiquitous nature of online social networks, they do come with their own set of problems and challenges. E.g., they represent a fertile ground for fake accounts and autonomous bots to spread fake news. Revealing whether some text content is written by a bot or a human would be of great value in the fight against the spreading of fake news and misinformation. In this paper, we address this problem using different Machine Learning (ML) techniques: conventional, Deep Learning (DL) based and Transfer Learning (TL) based. Using the dataset of the well‐known PAN 2019 Author Profiling Task, we show how relatively simple conventional ML methods can outperform DL and TL based ones for different languages (English and Spanish). In fact, our simplest model performs closely to the state‐of‐the‐art (SOTA) systems for the English language and even outperforms the SOTA systems for the Spanish language. [ABSTRACT FROM AUTHOR]

ABSTRACT In large-scale datasets, the researchers' multiple features need to be learned automatically instead of manually defined and enumerated, to improve the efficiency and effect of research collaboration prediction and recommendation. This paper applies the network embedding method to learn the context of each researcher by which the semantic similarities among researchers are calculated. Firstly, the co-authorship network is built in a large-scale dataset where research collaborations are denoted by co-authorships. Then the researchers' semantic contexts in the network are learned by the network embedding method based on deep learning, and each researcher's dense, low-dimensional vector is formed. Finally, the semantic similarities among researchers are calculated through vector similarity indices and quantitatively compared by link prediction. Experiments in the field of library and information science (LIS) verify that the method can improve the accuracy and effectiveness of research collaboration prediction and recommendation. [ABSTRACT FROM AUTHOR]

Transparency in authorship is a continuing topic in information science and scholarly communication. The process of determining authorship order in multi‐author publications, however, can be complicated. Authorship rubrics helping teams arrive at authorship order exist, but the extent to which certain roles are quantified (or not) and rewarded with authorship is unclear. This study examines eight authorship rubrics from five disciplines in the sciences and social sciences and evaluates their alignment with the Contributor Role Taxonomy (CRediT) framework; authorship rubrics are also compared on how they assign authorship credit and determine authorship order. Findings indicate that work on the methodology and the initial writing of the manuscript are most consistently quantified in authorship rubrics across the disciplines. Procedures for awarding authorship credit vary widely and methods for tie‐breaking rubric scores range from systematic to arbitrary. These findings suggest that regardless of discipline, contributions to carrying out and writing up research are seen as criteria for authorship. Differences in procedures for ordering authors may result, however, in different author order based on the chosen rubric. Ultimately, the fitness of use for authorship rubrics should be carefully considered by members of research teams, especially if the teams are comprised of interdisciplinary members. [ABSTRACT FROM AUTHOR]

Building upon well-established paradigms brought forth by such theorists as Robert K. Merton, Pierre Bourdieu, and Blaise Cronin, the panel will span the full cycle of academic production to show, through various bibliometric measures and other quantitative and qualitative analyses, how the reward system of science is evolving. While there is strong evidence to suggest that such forms of dissemination as social media output and blogging are being incorporated into scientific practices, scientific impact still remains principally assessed using measures such as authorship and citations, whilst other elements, such as acknowledgements, have received varying levels of regard at various times. Disciplinary considerations also arise. Using a wide range of approaches, measures, and datasets, the panelists will establish links between their individual research to create an empirically driven picture of the reward system of science and its indicators. Through the use of the Polldaddy application, audience members will answer questions and create an overview of their perception of the reward system of science. [ABSTRACT FROM AUTHOR]