Mayank Yuvaraj

This paper presents a novel interactive tool for sampling papers in bibliometric analysis and systematic reviews, integrating keyword frequency analysis and semantic similarity ranking. Built using R Shiny, the application enables researchers to prioritize academic abstracts through a dual-method approach systematically: Term frequency-inverse document frequency (TF-IDF) weighted keyword matching, and Cosine similarity-based semantic alignment with user-defined queries. A dynamic weighting mechanism enhances the hybrid approach, outperforming traditional methods by balancing lexical precision with contextual depth. The tool addresses critical challenges in quantitative literature review processes by introducing data-driven thresholding with three-tier prioritisation (green/orange/red categories) and export functionalities. In test cases, the hybrid approach classified 2.29 % of papers as highly relevant using keyword analysis and 1.47 % using semantic similarity on an AI in libraries dataset, with broader coverage (58.45% moderately relevant) in hybrid mode, demonstrating its ability to identify contextually aligned works efficiently. Technical implementation details, mathematical foundations, and applications are discussed. The tool supports extracting relevant papers from a dataset drawn from Web of Science, Scopus, OpenAlex, and Dimensions.

Through an altmetric analysis of publications from the top ten LIS journals, this study examines publicationpatterns, mentions across various SNSs, the geographical distribution of user engagement, and the distribution ofAAS for open-access versus closed-access articles. Data were sourced from Altmetric.com through Altmetric Exploreras indexed between January 1, 2014, and December 31, 2023, and analysed using MS Excel and SPSS. The studyfound a significant increase in research output and engagement until 2017, followed by a fluctuation and decline insubsequent years. The study also reveals that X (formerly Twitter) is the primary channel for research informationdissemination, with notable contributions from X handles of respective publishers and editors. The study also foundthat the most mentions are associated with the United States (30,791) and the United Kingdom (16,318) among194 countries. Additionally, the analysis shows that open-access and closed-access AAS do not follow a normaldistribution according to the Kolmogorov-Smirnov and Shapiro-Wilk tests. Overall findings highlight the importanceof SNSs in shaping academic communication and provide valuable information for researchers and stakeholders toenhance scholarly engagement and dissemination.

Background: The emergence of the artificial intelligence chatbot ChatGPT in November 2022 has garnered substantial attention across diverse disciplines. Despite widespread adoption in various sectors, the exploration of its application in libraries, especially within the medical domain, remains limited.Aims/Objectives: Many areas of interest remain unexplored like ChatGPT in medical libraries and this review aims to synthesise what is currently known about it to identify gaps and stimulate further research.Methods: Employing Cooper's integrative review method, this study involves a comprehensive analysis of existing literature on ChatGPT and its potential implementations within library contexts.Results: A systematic literature search across various databases yielded 166 papers, with 30 excluded for irrelevance. After abstract reviews and methodological assessments, 136 articles were selected. Critical Appraisal Skills Programme qualitative checklist further narrowed down to 29 papers, forming the basis for the present study. The literature analysis reveals diverse applications of ChatGPT in medical libraries, including aiding users in finding relevant medical information, answering queries, providing recommendations and facilitating access to resources. Potential challenges and ethical considerations associated with ChatGPT in this context are also highlighted.Conclusion: Positioned as a review, our study elucidates the applications of ChatGPT in medical libraries and discusses relevant considerations. The integration of ChatGPT into medical library services holds promise for enhancing information retrieval and user experience, benefiting library users and the broader medical community.

This study explores Indian library and information science (LIS) professionals’ perspectives on the integration of artificial intelligence (AI) in academic libraries in India. It aims to evaluate their comprehension of AI, determine their perspectives, investigate AI utilization, assess advantages, identify influencing factors, and examine attitudes towards AI adoption. A quantitative research approach was employed, utilizing a structured questionnaire designed based on study objectives and reviewed by subject matter experts. Purposive sampling targeted individuals with relevant LIS knowledge. Data were collected through Google Forms from 259 respondents and analysed using descriptive and inferential statistics. Respondents generally exhibited positive perceptions towards AI integration in libraries. High mean scores were observed for statements such as “AI can bridge librarian performance gaps” and “AI does not make library staff lazy.” Librarians expressed willingness to learn about AI, interest in its ethical implications, and confidence in its potential to improve library services. The study highlights a cautious optimism towards AI adoption in Indian academic libraries, with recognition of its potential benefits tempered by concerns about employment and resource allocation. Librarians demonstrate proactive attitudes towards engaging with AI technology and understanding its implications for library services, indicating a readiness to embrace AI within the profession.

Purpose: In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023. Design/methodology/approach: To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp. Findings: The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning. Originality/value: This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.

Purpose: Adopting and implementing robotic technology applications in the library is a significant technological up-gradation today. The purpose of this study was to evaluate selected literature focused mainly on robotics technology applications in the field of libraries and to assess the online social attention to research publications.Design/methodology/approach: The study employed Scientometric and altmetric tools to evaluate the research publications. The bibliographic data of research publications were downloaded from Scopus database and scrutinized one by one and 71 articles were selected which mainly focused on robotic technology in libraries. Altmetric data were collected from the Dimensions.ai database. The analysis was performed using MS Excel, Tableau, Biblioshiny, VOSviewer and SPSS software.Findings: Research on robotic technology in the field of libraries has been experiencing a gradual increase, marked by an annual growth rate of 12.93%. The United States has prominently led the way as the most active participant and collaborator in this advancement. Among the various journals, Library Hi Tech has notably stood out as a significant contributor to this field. However, the research articles have garnered limited social attention and impact. Furthermore, the patterns of authorship collaboration have demonstrated relatively modest levels within the field, and a weak correlation has been observed between the social attention received and the Scopus citation metrics of the publications.Practical implications: The research needs to be disseminated more through various social media platforms to increase its visibility. Sharing research information through social media can bridge the gap between academia and society. The findings of this study can serve as a valuable reference for researchers and policymakers.Originality/value: This study presents a Scientometric analysis of the selected published literature on robotics technology applications in the field of libraries, highlighting the progress and development of worldwide research in this area.

Purpose: There have been numerous publications on human monkeypox since it was reported. With the help of bibliometric analysis, this study examined research hotspots and future trends related to human monkeypox. Science mapping was used in this study to identify influential monkeypox researchers, institutions, articles, keywords, thematic structures, and clusters of articles.Design/methodology/approach: Based on a validated search query, bibliometric analysis of data collected from Web of Science from 1989 to September 2022 was conducted. Using the “Title-Keyword-Abstract” search option, the search query consisted of keywords “Monkeypox” OR “Monkeypox virus” OR “monkeypox” OR “monkey pox” OR “MPXV.” With the state-of-the-art tools Bibliometrix package of R Studio and VOSviewer, performance analysis and science mapping, as a part of standard bibliometric research of monkeypox research were conducted.Findings: Researchers published 708 monkeypox papers from 1989 to September 2022, with American researchers publishing 460 papers. Further, USA had the highest international cooperation in terms of collaborative research output. Centers for Disease Control and Prevention (CDC) is a global leader in monkeypox research since it is the most prolific and collaborative organization. There have been the most published papers on monkeypox in the Journal of Virology. Damon Inger K is also the most prolific and influential researcher in monkeypox research, with the highest number of publications and citations. In total, 1,679 keywords were identified in the study. From the cluster analysis four themes were identified in monkeypox research. They are (1) clinical features, (2) monkeypox virus epidemiology, (3) monkeypox virus vaccine defense, and (4) monkeypox virus-related treatment measures.Originality/value: Analysis of collaboration, findings, networks of research, and visualization separates this study from traditional metrics analysis. Currently, there are no similar studies with similar objectives based on the authors' knowledge.

The purpose of this paper is to highlight the importance of topic modelling in conducting literature reviews using the opensource LDAShiny package in the R environment, with green libraries literature as a case study. To conduct the analysis, a title and abstract dataset were prepared using the Scopus database and imported into the LDAShiny package for further analysis. It was found that the green libraries' literature ranged from 1989-2023, with a sharp increase in research topics since 2003. The study also identified key themes and documents associated with green libraries research, revealing that energy efficiency, waste reduction and recycling, and the use of sustainable materials have been extensively discussed in the literature. However, further research is needed on the implementation of these practices in libraries, as well as the impact of the COVID-19 pandemic on green libraries. The findings will be beneficial to researchers interested in using topic modelling for literature reviews.