The study maps information disorder research in library and information science through a combined scoping review and bibliometric analysis. Using Scopus-indexed journal articles, the study examines publication patterns and key contributors, identifies recurring topical clusters through keyword co-occurrence analysis, and analyzes the citation-based intellectual structure of the field through bibliographic coupling. The findings show that library and information science scholarship on information disorder has grown steadily, particularly in response to crisis-driven and platform-mediated information disruptions. The literature is organized around several recurring topical concentrations, including academic and civic contexts, misinformation and literacy responses, fake news and library response, pandemic-related information disorder, behavioral and critical perspectives, and information literacy and professional practice. Bibliographic coupling further indicates a connected intellectual structure in which newer studies remain anchored to an established citation base rather than forming sharply distinct trajectories. The results suggest that library and information science has engaged with information disorder primarily through literacy-oriented, professional, and institutional responses, while theoretical development remains uneven across the field. The study provides a clearer map of how information disorder has been taken up in library and information science and offers a foundation for future scholarship in this area.
Citation inequality in academic scholarship has attracted sustained attention, yet macro-level structural predictors remain underexplored in communication research, particularly those rooted in national and geographic differences. This study introduces the Cultural, Administrative, Geographic and Economic (CAGE) framework to examine how cultural, administrative, geographical, and economic distances between the country of the first author’s affiliation and the United States correlate with citation counts of 5695 articles published in 13 Social Sciences Citation Index Q1 communication journals from 2018 to 2024. Using hierarchical negative binomial regression and bootstrap-based mediation analysis, we find that cultural and geographical distances are directly and positively associated with citation counts, while administrative and economic distances are not. A dissemination–recognition trade-off in communication scholarship is revealed, where cultural, geographical, and economic distances suppress article views, which in turn reduce citations. International collaboration neither moderates the direct distance–citation relationships nor the indirect paths via views. These findings illuminate how geographic heterogeneity shapes scholarly recognition through distinct and opposing mechanisms.
Evaluating the international academic discourse power of Chinese English-language journals in the context of open science will enrich and refine the theoretical frameworks and indicator systems for academic journal assessment. Furthermore, the evaluation provides a scientific basis and decision support for promoting the international dissemination of China’s knowledge systems and enhancing the international academic discourse of Chinese English-language journals. First, this study analyses the intrinsic relationship between open science and the international academic discourse power of Chinese English-language journals. Second, drawing on Foucault’s theory of discourse power and Fei Xiaotong’s theory of cultural self-awareness, it delves into the formation process of the international academic discourse power of Chinese English-language journals. Third, by constructing a multidimensional evaluation indicator system encompassing four dimensions (discourse influence, discourse perceptibility, discourse dissemination capacity, and discourse leadership) and employing the entropy weighting method along with Projection Pursuit Classification, this study evaluates the international academic discourse power of 288 English-language journals in both a single dimension and a comprehensive approach. Finally, the effectiveness and reliability of the evaluation indicator system and evaluation results are validated through correlation analysis, the back propagation neural network model, and the Action Plan for Excellence in Chinese Scientific and Technical Journals. Furthermore, by formulating three major research hypotheses and drawing on relevant empirical findings, this study further systematically elucidates and tests the intrinsic mechanisms linking open science to the international academic discourse power of Chinese English-language journals.
The scientific literature surrounding the pioneering tool, Chat GPT, is vast and rapidly growing; however, the scope of research areas remains limited. The aim of the study is to conduct a bibliometric analysis to identify publications related to Chat GPT. Using the search terms ‘Chat GPT’ and ‘Chat GPT’, all publications related to Chat GPT from the Web of Science database were extracted. A descriptive analysis of 4177 publications from January 2021 to August 2024 was conducted by focusing on publication trends, influential authors, and research themes. A total of 4177 Chat GPT-related publications were published in 1697 different sources. The analysis shows that the majority of research concentrated in the fields of health sciences and applied sciences, with significant contributions from the United States, China, and Singapore. Notably, the most productive journal is the CUREUS Journal of Medical Science , while collaboration between authors is mainly found in developed countries. The study highlights key trends in Chat GPT–related publications, with the literature most frequently focusing on the fields of health sciences and applied sciences, while social sciences are less represented. This indicates a need for broader exploration of Chat GPT’s impact across various disciplines to fully understand its potential and implications.
Cross-platform recommender systems are gaining popularity for addressing cold start and data sparsity issues. However, existing research often assumes that data from the auxiliary platform can be fully leveraged, overlooking the inherent differences in services and the sensitivity of user-item interactions across platforms. Given the large-scale, noisy cross-platform data in this scenario, accurately identifying consistency and variability in user preferences is crucial. We propose the heterogeneous graph collaborative contrastive learning algorithm, which distinguishes users’ platform-core interests from platform-specific ones while capturing both global consistencies and local variations across platforms. Heterogeneous graph collaborative contrastive learning leverages the local and global views of a heterogeneous information network to model various general interests, while using two enhanced gated recurrent unit (GRU) networks to represent and transfer dynamic interests across platforms. In addition, heterogeneous graph collaborative contrastive learning introduces a collaborative contrastive learning mechanism that integrates and contrasts cross-platform and cross-view features to reduce redundancy and noise in cross-platform data while enriching the semantic depth of interest representations. We evaluate heterogeneous graph collaborative contrastive learning on the Weibo-Zhihu datasets, which contain 18,383 matched users, 17,802 items, and 768,539 user-item interaction records. Comprehensive experiments demonstrate that heterogeneous graph collaborative contrastive learning outperforms the best baseline model by 7.35% to 11.65% in hit rate @K and 10.18% to 26.12% in mean reciprocal rank @K.
This study proposes a wine recommendation system based on heterogeneous graph transformers that integrates hybrid node features from users, reviews, and wine knowledge. We use real-world data from a wine e-commerce platform, including 1685 wines, 12,361 users, 71,507 reviews, and 71,507 ratings. A multi-relational knowledge graph is constructed to represent relationships among wines, customers, and content. Node features are generated via decoding-enhanced BERT with disentangled attention–based review embeddings and structured wine attributes, while the graph structure captures both objective and subjective signals. We evaluate three heterogeneous graph transformer variants and benchmark them against the baseline light graph convolution network model. Our best-performing configuration, heterogeneous graph transformer model C with hybrid features, achieves an average improvement of 2.11% in precision and 5.87% in normalized discounted cumulative gain (NDCG) across top-K recommendations. These results highlight the value of feature diversity and graph-based modeling for enhancing personalized product recommendations in e-commerce.
This study examines the adoption, applications, benefits, and challenges of ChatGPT among research scholars at Aligarh Muslim University, offering empirical insights into the integration of generative artificial intelligence within academic research practices. Utilizing a quantitative survey with purposive sampling, data were collected from 191 scholars across diverse disciplinary strata. Findings indicate near-universal awareness of ChatGPT (99.48%) and predominant use of the free version (76.96%), with scholars from the Social Sciences representing the largest adopting group (38.20%). Primary uses included research idea generation (15.95%), knowledge queries (14.88%), and writing assistance (13.19%), framing ChatGPT as a cognitive augmentation tool. Key perceived benefits encompassed creative question formulation (M = 4.04), methodological understanding (M = 4.01), and writing enhancement (M = 4.00), alongside time savings (20.77%). Challenges highlighted response depth limitations (18.34%), accuracy issues (17.75%), over-reliance risks (17.55%), ethical concerns (14.99%), and institutional unreadiness (M = 4.17). Hypothesis testing indicated that ChatGPT adoption is associated with perceived usefulness and the depth of engagement, with trust-related factors acting as moderating variables. These findings are consistent with both technology acceptance and cognitive augmentation theoretical frameworks. Findings underscore rapid individual uptake outpacing institutional support, advocating AI literacy training, ethical governance frameworks, and standardized disclosure protocols to balance productivity gains with research integrity.
As digital transformation advances in depth, digital literacy has become increasingly crucial for sharing digital dividends and eradicating absolute poverty. Against this backdrop, this article establishes a digital literacy measurement framework using data from the 2018 China Family Panel Studies and empirically examines the peer effects of digital literacy. The results indicate that significant peer effects exist in residents’ digital literacy, which function through the mechanisms of learning behavior and neighborhood trust. In addition, peer effects help narrow the gap in digital literacy among residents, and they exert a stronger effect in reducing gaps in general and cognitive digital literacy than in applied and practical digital literacy. While peer effects effectively bridge intergenerational, urban–rural and status perception disparities in digital literacy, their impacts on gender, education, and income gaps are relatively limited. Peer effects present stronger effects in narrowing digital literacy gaps among the offspring, females, urban residents, low-education, high-income, and low-status groups than among the parental generation, males, rural residents, high-education, low-income, and high-status groups. These findings clarify the mechanism and heterogeneity of digital literacy peer effects and provide theoretical and practical implications for promoting residents’ digital literacy and bridging the digital divide.
In contrast to other disciplines, data reuse is less common in the social sciences. Online social cues (an indicator of a particular social significance) can provide a possible solution to this problem by providing additional information for data assessment and screening. This study generalized three cues (impression data cues, interaction data cues, and impression publisher cues) that serve the data reuse and three relative reuse criteria (data quality, data relevance, and source reliability). We focused on the influence of online social cues and their match effects with the criteria of perceived usefulness and reuse assessment performance. In total, 220 participants (41 young scholars and 179 students) were randomly sampled for a data reuse experiment, and their behavior, eye-movement, and perception data were collected using the experimental platform and eye-tracking device. Results confirmed the positive effects of cues and the match effects on enhancing users’ perceived usefulness and reuse assessment performance. This study shed light on the theoretical understanding of online social cues in improving data reuse and also provided practical implications for scientific data-sharing platforms to design different types of cues.
This article introduces ThemeScope, an analytical strategy for mapping the discursive traces of social representations across large-scale digital arenas. Grounded in social representation theory (SRT), it operationalises anchoring and objectification as discourse-analytic indicators computed ex post from co-occurrence structure and lexical concreteness. ThemeScope employs network analysis of word co-occurrences and visualisation techniques adapted from science mapping to identify and categorise lexical-thematic communities based on their structural organisation. We propose two quantitative measures to assess anchoring and objectification ex post and show the method’s interpretive potential within online discussions. This integrated framework bridges theoretical insight and computational scalability, providing a replicable approach to studying meaning-making processes in digital discourse.
This article aims to identify and analyze the knowledge structure and key trends within the field of cognitive technologies, providing insights for researchers and practitioners. Scientometric methods and co-word analysis were employed. In total, 446 articles published in the Web of Science database from 1975 to February 2025 were examined. In total, 1892 keywords were extracted and analyzed using word co-occurrence analysis to identify conceptual clusters and connections between them. The number of publications in cognitive technologies has significantly increased, demonstrating the growing importance of the field. Co-word analysis revealed six main conceptual clusters and 354 connections between them, with a total link strength of 536. These clusters encompass topics such as cognitive technologies, artificial intelligence, machine learning, cognitive computing, deep learning, big data, the Internet of Things, neural networks, natural language processing, and computer vision, highlighting the interdisciplinary nature of the field. Cognitive technologies are strongly interconnected with various scientific and technological domains. This research provides a comprehensive overview of the knowledge map and research trends in this field. The findings can assist researchers in identifying research gaps and future directions, and inform policymakers in making decisions regarding development and investment in cognitive technologies.
Given the growing importance of academic search engine optimization (A-SEO) in enhancing research visibility, it is critical to understand how different SEO analytics tools impact quantitative research findings. This study compares four leading SEO analytics tools - Ahrefs, SEMrush, Serpstat, and Ubersuggest - using data from two well-known academic open-access mega publishers (MDPI and Frontiers) to assess the similarities of the web metrics provided, specifically organic keyword counts, keyword search volumes, and URL-based web traffic. Results show significant variation in the data, with a low overlap in organic keywords and URL-based web traffic across tools, suggesting that tool selection significantly determines findings. The study also highlights methodological challenges when comparing SEO tools, most notably differences in filtering procedures that jeopardize a rigorous comparison of tools. This work provides valuable insights for researchers and reviewers, offering guidelines to improve research designs and enhance A-SEO research transparency.
This study investigates the perceptions of Brazilian researchers in agronomy regarding the order of authorship and the role of the corresponding author in scientific publications utilizing a survey of 380 responses. Key findings reveal that most researchers see the first author as the primary executor of the research, and the last author as the leader. The study also highlights a significant association between authorship order, research credibility, and citation impact, with differing views based on the type of institutional affiliation. In addition, the survey shows a preference for the corresponding author to be either the first or the last author, emphasizing their role in communication with journals. The results suggest that while the authorship order is crucial for recognizing contributions, the corresponding author's role is primarily seen as a formality. The study calls for more precise guidelines on authorship practices and further research into authorship dynamics in different scientific fields.
Accessing information through eHealth technologies is typically suggested to be empowering. This study challenges this idea and explores individuals' perceptions of patient portals and how the variation in these perceptions can be explained in terms of different orders of empowerment. The study extends conceptual understanding of (dis)empowerment as an outcome of patient portal use. It takes an information perspective and draws from an analysis of data from a population-level survey study of older adults (55-70 years) in Finland (N = 251). The questionnaire included questions on health information seeking and management, health behaviour as well as the use and views of current and future eHealth technologies available through patient portals. The data was analysed using exploratory factor analysis. The findings show how individuals can get empowered and disempowered in multiple ways by eHealth technologies. Four forms or orders of (dis)empowerment were identified: disempowerment, knowledge empowerment, emotional empowerment, and information and communication empowerment. Rather than assuming that empowerment is a monolithic outcome of technology use, this study underlines the importance of acknowledging the presence of different orders of (dis)empowerment as means to the diverse ends that individuals have in their lives.
With the rapid adoption of generative artificial intelligence (AI) featuring empathetic expressions in health consultation contexts, concerns have emerged regarding the risk of health misinformation dissemination alongside improved interaction quality. Grounded in the stimulus-organism-response (S-O-R) framework, this study investigates how perceived empathy and interaction modalities influence users' acceptance intention towards AI-generated health misinformation through underlying psychological mechanisms. A 2 & times; 2 scenario-based experiment was conducted by manipulating AI empathy (present vs absent) and interaction modality (text vs voice). Survey data collected from Chinese adult users were analysed using analysis of variance and mediation analysis. The results indicate that perceived empathy significantly increases users' acceptance intention of health misinformation, both directly and indirectly through perceived usefulness and perceived social presence. Interaction modality does not exert a direct effect but influences acceptance intention indirectly via these perceptual factors. The findings reveal a 'double-edged sword' effect of empathetic AI in health information contexts: while enhancing perceived value and social presence, empathetic design may also reduce users' epistemic vigilance and amplify misinformation risks. This study provides theoretical insights and practical implications for the responsible design and governance of empathetic AI systems in health communication.
Open data is reshaping research by enhancing transparency, reproducibility, and collaboration. This review analyzes more than 120 peer-reviewed studies (2021-2025) to evaluate their impact on academic collaboration and productivity, especially in emerging countries like Saudi Arabia. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we find consistent links between open data and increased citations, co-authorship, and interdisciplinary work, for example, genome-wide studies with shared data saw up to 81.8% more citations. However, openness is uneven: environmental and life sciences lead in principles of Findable, Accessible, Interoperable, and Reusable data (FAIR) compliance, while engineering and materials science trail. North America and Europe dominate open data infrastructure, although Saudi Arabia shows policy-driven progress under Vision 2030. Key barriers include data quality concerns, lack of incentives, ethical constraints, and limited infrastructure in low-resource contexts. This review highlights thematic patterns, visualizes trends, and offers recommendations to improve practices and foster inclusive global research collaboration.
This study examines the role of information practices in shaping research capacity building (RCB) among female academics in highly segregated higher education environments, with a focus on the Middle East. Building on the workplace information practices (WIP) framework as a theoretical lens, the study investigates the challenges and opportunities that influence female academics' trajectories and identifies strategies to enhance equality of opportunity and research outcomes. The study draws on semi-structured interviews with 57 female academics across four groups: those studying overseas, those returning from overseas study, those studying locally and those employed in research departments. The findings reveal three interrelated categories of challenge: cultural (primarily gender discrimination), social (notably family responsibilities) and academic (including limited language competence, research skills and information literacy). Interviewees also identified opportunities at individual, professional and institutional levels that strengthen RCB, including targeted research training, enhanced facilities, reduced workloads and knowledge transfer informed by international best practices. This study makes two key contributions. First, it extends the WIP framework by incorporating the specific dynamics of gender segregation and cultural constraints, offering a contextualised understanding of information practices in marginalised academic communities. Second, it develops a new, empirically grounded information practices framework tailored to the Saudi higher education context. By foregrounding the lived experiences of female academics, the study provides original insights into the intersection of gender, culture and institutional structures, offering a practical and theoretical model for advancing research capacity and enabling gender equity in higher education.
In scientific social networks, academic groups play a significant role in facilitating collaboration between researchers and promoting the dissemination of papers, which provides unique opportunities for paper recommendation. However, existing paper recommendation methods rarely consider the valuable group information, which limits their potential for improving recommendation performance. In this article, a novel multi-graph fusion network with attention mechanism (GI-MFA) is proposed for paper recommendation considering group information. First, the group-researcher bipartite graph, the researcher-paper bipartite graph and the group-paper bipartite graph are constructed to model the relationships between researchers, papers and groups. Graph neural networks are used to learn the embeddings of researchers and papers at both the individual and group levels across these bipartite graphs. Second, to effectively fuse the individual-level and group-level embeddings, we introduce researcher-wise attention and paper-wise attention mechanisms. To verify the effectiveness of GI-MFA, experiments are conducted on a real-world dataset CiteULike. The experimental results demonstrate the superiority of GI-MFA over all baselines.
This article presents an original model for assessing scientific productivity, research power ranking (RPR), which is based on the adaptation of the Elo-rating system to the context of scientific activity. Unlike traditional scientometric indicators, RPR accounts for the dynamics, multidimensionality, and relativity of research power. The model comprises three components-fundamental, applied, and commercial activity-each represented by a separate rating and updated on the basis of probabilistic "scientific games" analogous to chess matches. The scientific rating of each researcher is calculated as a weighted sum of the components, allowing the model to reflect not only their current position but also their career trajectory, including phase transitions, breakthroughs, and changes in scientific style. Numerical simulations were conducted for both the individual trajectories and population-level distributions of the researchers. Phase diagrams were constructed, and a typology of scientific styles was formulated. The results demonstrate that RPR can serve as a universal tool for objective assessment, strategic planning, and visualization of scientific reputation in both academic and applied environments.
Purpose: This study maps the intellectual and thematic evolution of research on digital publishing and open access (OA) in Library and Information Science (LIS) between 2020 and 2025, identifying major trends, influential contributors, and emerging frontiers.Methods: A bibliometric and science-mapping analysis was conducted on 1849 publications indexed in the Web of Science Core Collection. Performance indicators captured productivity and citation patterns, while science mapping examined co-authorship, co-citation, and keyword co-occurrence networks.Results: Publications show steady growth, peaking in 2024, with the United States, China, and the United Kingdom as leading contributors. A core-periphery authorship structure is anchored by prolific scholars such as Abrizah A, Xu J, Jamali HR, and Nicholas D. Highly cited works in JASIST, Journal of Academic Librarianship, and Library Hi Tech form the intellectual backbone of the field. Thematic mapping reveals continuity in topics such as academic libraries and scholarly communication, alongside newer themes including artificial intelligence, equity, and open science. COVID-19 temporarily reshaped research dynamics (2020-2022), highlighting the community's responsiveness to global disruptions.Implications: The study deepens understanding of LIS research dynamics within Web of Science-indexed venues and offers guidance for libraries, policymakers, and publishers. It highlights the need to foster context-sensitive OA models, support more equitable participation in APC-driven environments, and integrate technological innovation into scholarly communication infrastructures.
