Human respiratory infections caused by coronaviruses can range from mild to deadly. Although there are numerous studies on coronavirus disease 2019 (COVID-19), few have been published on its Omicron variant. In order to remedy this deficiency, this study undertook a bibliometric analysis of the publishing patterns of studies on the Omicron variant and identified hotspots. Automated transportation, environmental protection, improved healthcare, innovation in banking, and smart homes are just a few areas where machine learning has found use in tackling complicated problems. The sophisticated Scopus database was queried for papers with the term “Omicron” in the title published between January 2020 and June 2022. Microsoft Excel 365, VOSviewer, Bibliometrix, and Biblioshiny from R were used for a statistical analysis of the publications. Over the study period, 1917 relevant publications were found in the Scopus database. Viruses was the most popular in publications for Omicron variant research, with 150 papers published, while Cell was the most cited source. The bibliometric analysis determined the most productive nations, with USA leading the list with the highest number of publications (344) and the highest level of international collaboration on the Omicron variant. This study highlights scientific advances and scholarly collaboration trends and serves as a model for demonstrating global trends in Omicron variant research. It can aid policymakers and medical researchers to fully grasp the current status of research on the Omicron variant. It also provides normative data on the Omicron variant for visualization, study, and application.

This study has conducted a meta-literature review examining the past, present and possible future trends of Fintech research using 360 selected articles published between 2006 and June 2020. Both quantitative and qualitative techniques were applied. In the quantitative approach, a bibliometric citation analysis using HistCite and VOSviewer software was conducted and the qualitative analysis covered the identification of four main research streams related to (i) Bitcoin and digital currency, (ii) crowdfunding, (iii) mobile payment, and (iv) blockchain. The study results highlight the most influential aspects of the FinTech literature, such as the leading countries, institutions, journals, authors, and articles. Suggestions for the potential future direction of FinTech literature have also been made.

This systematic review synthesizes a decade of peer-reviewed research (2015–2025) examining the interplay of filter bubbles, echo chambers, and algorithmic bias in shaping youth engagement within social media. A total of 30 studies were analyzed, using the PRISMA 2020 framework, encompassing computational audits, simulation modeling, surveys, ethnographic accounts, and mixed-methods designs across diverse platforms, including Facebook, YouTube, Twitter/X, Instagram, TikTok, and Weibo. Results reveal three consistent patterns: (i) algorithmic systems structurally amplify ideological homogeneity, reinforcing selective exposure and limiting viewpoint diversity; (ii) youth demonstrate partial awareness and adaptive strategies to navigate algorithmic feeds, though their agency is constrained by opaque recommender systems and uneven digital literacy; and (iii) echo chambers not only foster ideological polarization but also serve as spaces for identity reinforcement and cultural belonging. Despite these insights, the evidence base suffers from geographic bias toward Western contexts, limited longitudinal research, methodological fragmentation, and conceptual ambiguity in key definitions. This review highlights the need for integrative, cross-cultural, and youth-centered approaches that bridge empirical evidence with lived experiences.

Evidence from well-established theories of various disciplines manifests that, in the current technology-led world, continued professional development (CPD) of information professionals plays a paramount role in the uplift of institutions. CPD of university library professionals via e-learning programs leads to the implementation of user-centric-services through the initiation of emerging technological tools and the latest methods of service-delivery. The focus of this study is to shed light on the factors influencing e-learning for CPD of working librarians, challenges being encountered for e-learning adoption, and to propose the best practices for designing an efficient e-learning portfolio. For meeting the focused study-objectives, the authors applied PRISMA guidelines and procedures. An extensive search was conducted utilizing the world’s 16 leading e-databases and digital tools containing the most relevant core studies. Consequently, 30 impact factor research papers published in renowned databases were included through the identification, screening, eligibility, and inclusion process. Findings revealed that different factors—including organizational survival, continuous changes, adoption of emerging technologies, and professional growth—encouraged e-learning for CPD of information professionals. The study results showed that four main challenges—technical difficulties, lack of funds, reliance upon conventional models, and overwhelming work-load—were encountered for e-learning adoption. The authors proposed a framework for the development of an effective and efficient e-learning portfolio for building professional expertise among university librarians to support the organizational vision and mission statement. The recommended framework is based upon emergent categories and sub-categories extracted via thematic analysis of the existing empirical studies. This study has theoretical insights for the researchers through valuable addition in the body of literature and practical considerations for policy implementers to construct sustainable policies for devising e-learning programs to develop professional expertise in the university library workforce for attainment of value-added outcomes.

Purpose: The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection. Design/methodology/approach: “Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review. Findings: Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news. Originality/value: The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.

The objectives of the study were to identify the relationship between big data analytics with context-based news detection on digital media in the data age, to find out the trending approaches to detect fake news on digital media, and to explore the challenges for constructing quality big data to detect misinformation on social media. Scoping review methodology was applied to carry out a content analysis of 42 peer-reviewed research papers published in 10 world-leading digital databases. Findings revealed a strong positive correlation between quality big data analytics and fake news detection on digital media. Additionally, it was found that artificial intelligence, fact-checking sites, neural networks, and new media literacy are trending techniques to identify correct information in the age of misinformation. Moreover, results manifested that hidden agenda, the volume of fake information on digital media, massive unstructured data, the fast spread of fake news on digital media, and fake user accounts are prevalent challenges to construct authentic big data for detecting false online information on digital media platforms. Theoretically, the study has added valuable literature to the existing body of knowledge by exploring the relationship between big data analytics and context-based fake news on digital media in the data age. This intellectual piece also contributes socially by offering practical recommendations to control the cancer of fake news in society for stopping horrific perils; hence, it has a societal impact. Current research has practical applications for generators of digital media applications, policy-makers, decision-takers, government representatives, civil societies, higher education bodies, media workforce, educationists, and all other stakeholders. Recommendations offered in the paper are a roadmap for framing impactful policies to stay away from the harms of fake digital news.