Informatics

40 pages, 1387 KB

Open AccessArticle

Digital Skills and Employer Transparency: Two Key Drivers Reinforcing Positive AI Attitudes and Perception Among Europeans

by Dharan Bharti, Cristian Balducci and Salvatore Zappalà

Informatics 2026, 13(1), 17; https://doi.org/10.3390/informatics13010017 - 22 Jan 2026

Viewed by 186

Abstract

Using 2024 Eurobarometer survey data from 26,415 workers in 27 EU countries, this study examines how digital skills and employer transparency shape workers’ attitudes toward and perception of artificial intelligence (AI). Drawing on information systems and behavioral theories, regression analyses reveal that digital [...] Read more.

Using 2024 Eurobarometer survey data from 26,415 workers in 27 EU countries, this study examines how digital skills and employer transparency shape workers’ attitudes toward and perception of artificial intelligence (AI). Drawing on information systems and behavioral theories, regression analyses reveal that digital skills strongly predict augmentation-dominant attitude. Workers with higher digital skills view AI as complementary rather than threatening, with an augmentation attitude mediating 56% of the skills–perception relationship. Adjacently, employer transparency attenuates the translation of replacement attitude into a negative perception of AI in the workplace. Organizations and policymakers should prioritize digital upskilling and ensure workplace AI transparency requirements to foster a positive attitude and perception, recognizing that skills development and organizational communication are equally vital for the successful integration of AI in the workplace. Full article

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21 pages, 1961 KB

Open AccessArticle

Design and Evaluation of a Generative AI-Enhanced Serious Game for Digital Literacy: An AI-Driven NPC Approach

by Suepphong Chernbumroong, Kannikar Intawong, Udomchoke Asawimalkit, Kitti Puritat and Phichete Julrode

Informatics 2026, 13(1), 16; https://doi.org/10.3390/informatics13010016 - 21 Jan 2026

Viewed by 248

Abstract

The rapid proliferation of misinformation on social media underscores the urgent need for scalable digital-literacy instruction. This study presents the design and evaluation of a Generative AI-enhanced serious game system that integrates Large Language Models (LLMs) to drive adaptive non-player characters (NPCs). Unlike [...] Read more.

The rapid proliferation of misinformation on social media underscores the urgent need for scalable digital-literacy instruction. This study presents the design and evaluation of a Generative AI-enhanced serious game system that integrates Large Language Models (LLMs) to drive adaptive non-player characters (NPCs). Unlike traditional scripted interactions, the system employs role-based prompt engineering to align real-time AI dialogue with the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) framework, enabling dynamic scaffolding and authentic misinformation scenarios. A mixed-method experiment with 60 undergraduate students compared this AI-driven approach to traditional instruction using a 40-item digital-literacy pre/post test, the Intrinsic Motivation Inventory (IMI), and open-ended reflections. Results indicated that while both groups improved significantly, the game-based group achieved larger gains in credibility-evaluation performance and reported higher perceived competence, interest, and effort. Qualitative analysis highlighted the HCI trade-off between the high pedagogical value of adaptive AI guidance and technical constraints such as system latency. The findings demonstrate that Generative AI can be effectively operationalized as a dynamic interface layer in serious games to strengthen critical reasoning. This study provides practical guidelines for architecting AI-NPC interactions and advances the theoretical understanding of AI-supported educational informatics. Full article

(This article belongs to the Special Issue Generative AI in Higher Education: Applications, Implications, and Future Directions)

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17 pages, 7858 KB

Open AccessArticle

Sensor-Drift Compensation in Electronic-Nose-Based Gas Recognition Using Knowledge Distillation

by Juntao Lin and Xianghao Zhan

Informatics 2026, 13(1), 15; https://doi.org/10.3390/informatics13010015 - 20 Jan 2026

Viewed by 199

Abstract

Environmental changes and sensor aging can cause sensor drift in sensor array responses (i.e., a shift in the measured signal/feature distribution over time), which in turn degrades gas classification performance in real-world deployments of electronic-nose systems. Previous studies using the UCI Gas Sensor [...] Read more.

Environmental changes and sensor aging can cause sensor drift in sensor array responses (i.e., a shift in the measured signal/feature distribution over time), which in turn degrades gas classification performance in real-world deployments of electronic-nose systems. Previous studies using the UCI Gas Sensor Array Drift Dataset as a benchmark reported promising drift compensation results but often lacked robust statistical validation and may overcompensate for drift by suppressing class-discriminative variance. To address these limitations and rigorously evaluate improvements in sensor-drift compensation, we designed two domain adaptation tasks based on the UCI electronic-nose dataset: (1) using the first batch to predict remaining batches, simulating a controlled laboratory setting, and (2) using Batches 1 through

n - 1

to predict Batch n, simulating continuous training data updates for online training. Then, we systematically tested three methods—our semi-supervised knowledge distillation method (KD) for sensor-drift compensation; a previously benchmarked method, Domain-Regularized Component Analysis (DRCA); and a hybrid method, KD–DRCA—across 30 random test-set partitions on the UCI dataset. We showed that semi-supervised KD consistently outperformed both DRCA and KD–DRCA, achieving up to 18% and 15% relative improvements in accuracy and F1-score, respectively, over the baseline, proving KD’s superior effectiveness in electronic-nose drift compensation. This work provides a rigorous statistical validation of KD for electronic-nose drift compensation under long-term temporal drift, with repeated randomized evaluation and significance testing, and demonstrates consistent improvements over DRCA on the UCI drift benchmark. Full article

(This article belongs to the Section Machine Learning)

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38 pages, 8329 KB

Open AccessReview

The Validation–Deployment Gap in Agricultural Information Systems: A Systematic Technology Readiness Assessment

by Mary Elsy Arzuaga-Ochoa, Melisa Acosta-Coll and Mauricio Barrios Barrios

Informatics 2026, 13(1), 14; https://doi.org/10.3390/informatics13010014 - 19 Jan 2026

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Abstract

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols [...] Read more.

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80–92%, while blockchain implementations (15.2%) show fast transaction times (<2 s) but limited real-world adoption. Machine learning models using Random Forest, Gradient Boosting, and CNNs reach 85–95% predictive accuracy, and IoT systems report >95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL ≤ 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an “efficiency paradox”: strong technical performance (75–97/100) contrasts with weak economic validation (≤20% include cost–benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation–deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions. Full article

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23 pages, 1548 KB

Open AccessArticle

New Concept of Digital Learning Space for Health Professional Students: Quantitative Research Analysis on Perceptions

by Joshua Mincheol Kim, Provides Tsing Yin Ng, Netaniah Kisha Pinto, Kenneth Chung Hin Lai, Evan Yu Tseng Wu, Olivia Miu Yung Ngan, Charis Yuk Man Li and Florence Mei Kuen Tang

Informatics 2026, 13(1), 13; https://doi.org/10.3390/informatics13010013 - 15 Jan 2026

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Abstract

The Immersive Decentralized Digital space (IDDs), derived from blockchain technology and Massively Multiplayer Online Games (MMOGs), enables real-time multisensory interactions that support social connection under metaverse concepts. Although recognized as a technology with significant potential for educational innovation, IDDs remain underutilized in health [...] Read more.

The Immersive Decentralized Digital space (IDDs), derived from blockchain technology and Massively Multiplayer Online Games (MMOGs), enables real-time multisensory interactions that support social connection under metaverse concepts. Although recognized as a technology with significant potential for educational innovation, IDDs remain underutilized in health professions education. Health profession students are often unaware of how IDDs’ features can be applied to their learning through in- or after-classroom activities. This study employs a quantitative research design to evaluate students’ perceptions of next-generation digital learning without any prior exposure to IDDs. An electronic survey was developed to examine four dimensions of learning facilitation: “Remote Learning” for capturing past experiences with digital competence during the COVID-19 era; “Digital Evolution,” reflecting preferences in utilizing digital spaces; “Interactive Communication” and “Knowledge Application” for applicability of IDDs in the health professions education. Statistical analyses revealed no significant differences in perceptions based on gender or major on all factors. Nevertheless, significant differences emerged based on nationality in “Digital Evolution”, “Interactive Communication”, and “Knowledge Application”, highlighting the influence of cultural and educational backgrounds on receptiveness to virtual learning environments. By recognizing the discrepancies and addressing barriers to digital inclusion, IDDs hold strong potential to enhance health professional learning experiences and educational outcomes. Full article

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21 pages, 4132 KB

Open AccessArticle

Can Location-Based Augmented Reality Support Cultural-Heritage Experience in Real-World Settings? Age-Related Engagement Patterns and a Field-Based Evaluation

by Phichete Julrode, Darin Poollapalin, Sumalee Sangamuang, Kannikar Intawong and Kitti Puritat

Informatics 2026, 13(1), 12; https://doi.org/10.3390/informatics13010012 - 15 Jan 2026

Viewed by 285

Abstract

The Wua-Lai silvercraft community in Chiang Mai is experiencing a widening disconnect with younger visitors, raising concerns about the erosion of intangible cultural heritage. This study evaluates “Silver Craft Journey,” a location-based augmented reality (LBAR) system designed to revitalize cultural engagement and enhance [...] Read more.

The Wua-Lai silvercraft community in Chiang Mai is experiencing a widening disconnect with younger visitors, raising concerns about the erosion of intangible cultural heritage. This study evaluates “Silver Craft Journey,” a location-based augmented reality (LBAR) system designed to revitalize cultural engagement and enhance cultural-heritage experience through context-aware, gamified exploration. A quasi-experimental field study with 254 participants across three age groups examined the system’s impact on cultural-heritage experience, knowledge acquisition, and real-world engagement. Results demonstrate substantial knowledge gains, with a mean increase of 7.74 points (SD = 4.37) and a large effect size (Cohen’s d = 1.77), supporting the effectiveness of LBAR in supporting tangible and intangible heritage understanding. Behavioral log data reveal clear age-related engagement patterns: older participants (41–51) showed declining mission completion rates and reduced interaction times at later points of interest, which may reflect increased cognitive and physical demands during extended AR navigation under real-world conditions. These findings underscore the potential of location-based AR to enhance cultural-heritage experience in real-world settings while highlighting the importance of age-adaptive interaction and route-design strategies. The study contributes a replicable model for integrating digital tourism, embodied AR experience, and community-based heritage preservation. Full article

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12 pages, 216 KB

Open AccessBrief Report

Enhancing Interactive Teaching for the Next Generation of Nurses: Generative-AI-Assisted Design of a Full-Day Professional Development Workshop

by Su-I Hou

Informatics 2026, 13(1), 11; https://doi.org/10.3390/informatics13010011 - 15 Jan 2026

Viewed by 304

Abstract

Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for [...] Read more.

Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for nursing faculty, senior clinical nurses, and nurse leaders, developed using a design-thinking approach supported by generative AI. Methods: The workshop comprised four thematic sessions: (1) Learning styles across generations, (2) Interactive teaching methods, (3) Application of interactive teaching strategies, and (4) Lesson planning and transfer. Generative AI was used during planning to create icebreakers, discussion prompts, clinical teaching scenarios, and application templates. Design decisions emphasized low-tech, low-prep strategies suitable for spontaneous clinical teaching, thereby reducing barriers to adoption. Activities included emoji-card introductions, quick generational polls, colored-paper reflections, portable whiteboard brainstorming, role plays, fishbowl discussions, gallery walks, and movement-based group exercises. Participants (N = 37) were predominantly female (95%) and represented multiple generations of X, Y, and Z. Mid- and end-of-workshop reflection prompts were embedded within Sessions 2 and 4, with participants recording their responses on colored papers, which were then compiled into a single Word document for thematic analysis. Results: Thematic analysis of 59 mid- and end-workshop reflections revealed six interconnected themes, grouped into three categories: (1) engagement and experiential learning, (2) practical applicability and generational awareness, and (3) facilitation, environment, and motivation. Participants emphasized the workshop’s lively pace and hands-on design. Experiencing strategies firsthand built confidence for application, while generational awareness encouraged reflection on adapting methods for younger learners. The facilitator’s passion, personable approach, and structured use of peer learning created a psychologically safe and motivating climate, leaving participants recharged and inspired to integrate interactive methods. Discussion: The workshop illustrates how AI-assisted, design-thinking-driven professional development can model effective strategies for next-generation learners. When paired with skilled facilitation, AI-supported planning enhances engagement, fosters reflective practice, and promotes immediate transfer of interactive strategies into diverse teaching settings. Full article

(This article belongs to the Special Issue Generative AI in Higher Education: Applications, Implications, and Future Directions)

26 pages, 1167 KB

Open AccessReview

A Review of Multimodal Sentiment Analysis in Online Public Opinion Monitoring

by Shuxian Liu and Tianyi Li

Informatics 2026, 13(1), 10; https://doi.org/10.3390/informatics13010010 - 14 Jan 2026

Viewed by 470

Abstract

With the rapid development of the Internet, online public opinion monitoring has emerged as a crucial task in the information era. Multimodal sentiment analysis, through the integration of multiple modalities such as text, images, and audio, combined with technologies including natural language processing [...] Read more.

With the rapid development of the Internet, online public opinion monitoring has emerged as a crucial task in the information era. Multimodal sentiment analysis, through the integration of multiple modalities such as text, images, and audio, combined with technologies including natural language processing and computer vision, offers novel technical means for online public opinion monitoring. Nevertheless, current research still faces many challenges, such as the scarcity of high-quality datasets, limited model generalization ability, and difficulties with cross-modal feature fusion. This paper reviews the current research progress of multimodal sentiment analysis in online public opinion monitoring, including its development history, key technologies, and application scenarios. Existing problems are analyzed and future research directions are discussed. In particular, we emphasize a fusion-architecture-centric comparison under online public opinion monitoring, and discuss cross-lingual differences that affect multimodal alignment and evaluation. Full article

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30 pages, 4733 KB

Open AccessArticle

Knowledge Organization of Buddhist Learning Resources for Tourism: Virtual Tour of Wat Phra Pathom Chedi

by Bulan Kulavijit, Wirapong Chansanam, Kannikar Intawong and Kitti Puritat

Informatics 2026, 13(1), 9; https://doi.org/10.3390/informatics13010009 - 13 Jan 2026

Viewed by 181

Abstract

This study curates and structures knowledge concerning Buddhist learning resources for tourism, presenting it through a virtual tour of Wat Phra Pathom Chedi Ratchaworamahawihan in Nakhon Pathom Province. Employing a mixed-methods approach that integrates both qualitative and quantitative methodologies, the research first establishes [...] Read more.

This study curates and structures knowledge concerning Buddhist learning resources for tourism, presenting it through a virtual tour of Wat Phra Pathom Chedi Ratchaworamahawihan in Nakhon Pathom Province. Employing a mixed-methods approach that integrates both qualitative and quantitative methodologies, the research first establishes a structured knowledge base. This involves developing a comprehensive metadata schema for cataloging the temple’s diverse resources, including both sacred sites and artifacts, to enhance their searchability and accessibility. Subsequently, this knowledge is rendered into a virtual tour, which serves as an exemplary model of a Buddhist digital learning resource for tourism. The findings reveal the extensive diversity of resources within the temple. The developed virtual tour platform allows users an immersive exploration of the site via 360-degree panoramic views. This research presents significant implications for relevant agencies, offering a scalable model for the digital dissemination of cultural heritage. It is anticipated that this initiative will expand global access to and appreciation of the temple’s cultural value, thereby fostering international interest in visitation. Such engagement is poised to stimulate the local economy and bolster Thailand’s image as a premier cultural tourism destination. Full article

(This article belongs to the Special Issue Real-World Applications and Prototyping of Information Systems for Extended Reality (VR, AR, and MR))

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16 pages, 947 KB

Open AccessArticle

Depression Detection Method Based on Multi-Modal Multi-Layer Collaborative Perception Attention Mechanism of Symmetric Structure

by Shaorong Jiang, Chengjun Xu and Xiuya Fang

Informatics 2026, 13(1), 8; https://doi.org/10.3390/informatics13010008 - 12 Jan 2026

Viewed by 272

Abstract

Depression is a mental illness with hidden characteristics that affects human physical and mental health. In severe cases, it may lead to suicidal behavior (for example, among college students and social groups). Therefore, it has attracted widespread attention. Scholars have developed numerous models [...] Read more.

Depression is a mental illness with hidden characteristics that affects human physical and mental health. In severe cases, it may lead to suicidal behavior (for example, among college students and social groups). Therefore, it has attracted widespread attention. Scholars have developed numerous models and methods for depression detection. However, most of these methods focus on a single modality and do not consider the influence of gender on depression, while the existing models have limitations such as complex structures. To solve this problem, we propose a symmetric-structured, multi-modal, multi-layer cooperative perception model for depression detection that dynamically focuses on critical features. First, the double-branch symmetric structure of the proposed model is designed to account for gender-based variations in emotional factors. Second, we introduce a stacked multi-head attention (MHA) module and an interactive cross-attention module to comprehensively extract key features while suppressing irrelevant information. A bidirectional long short-term memory network (BiLSTM) module enhances depression detection accuracy. To verify the effectiveness and feasibility of the model, we conducted a series of experiments using the proposed method on the AVEC 2014 dataset. Compared with the most advanced HMTL-IMHAFF model, our model improves the accuracy by 0.0308. The results indicate that the proposed framework demonstrates superior performance. Full article

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30 pages, 1341 KB

Open AccessArticle

A Novel MBPSO–BDGWO Ensemble Feature Selection Method for High-Dimensional Classification Data

by Nuriye Sancar

Informatics 2026, 13(1), 7; https://doi.org/10.3390/informatics13010007 - 12 Jan 2026

Viewed by 321

Abstract

In a high-dimensional classification dataset, feature selection is crucial for improving classification performance and computational efficiency by identifying an informative subset of features while reducing noise, redundancy, and overfitting. This study proposes a novel metaheuristic-based ensemble feature selection approach by combining the complementary [...] Read more.

In a high-dimensional classification dataset, feature selection is crucial for improving classification performance and computational efficiency by identifying an informative subset of features while reducing noise, redundancy, and overfitting. This study proposes a novel metaheuristic-based ensemble feature selection approach by combining the complementary strengths of Modified Binary Particle Swarm Optimization (MBPSO) and Binary Dynamic Grey Wolf Optimization (BDGWO). The proposed MBPSO–BDGWO ensemble method is specifically designed for high-dimensional classification problems. The performance of the proposed MBPSO–BDGWO ensemble method was rigorously evaluated through an extensive simulation study under multiple high-dimensional scenarios with varying correlation structures. The ensemble method was further validated on several real datasets. Comparative analyses were conducted against single-stage feature selection methods, including BPSO, BGWO, MBPSO, and BDGWO, using evaluation metrics such as accuracy, the F1-score, the true positive rate (TPR), the false positive rate (FPR), the AUC, precision, and the Jaccard stability index. Simulation studies conducted under various dimensionality and correlation scenarios show that the proposed ensemble method achieves a low FPR, a high TPR/Precision/F1/AUC, and strong selection stability, clearly outperforming both classical and advanced single-stage methods, even as dimensionality and collinearity increase. In contrast, single-stage methods typically experience substantial performance degradation in high-correlation and high-dimensional settings, particularly BPSO and BGWO. Moreover, on the real datasets, the ensemble method outperformed all compared single-stage methods and produced consistently low MAD values across repetitions, indicating robustness and stability even in ultra-high-dimensional genomic datasets. Overall, the findings indicate that the proposed ensemble method demonstrates consistent performance across the evaluated scenarios and achieves higher selection stability compared with the single-stage methods. Full article

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17 pages, 1585 KB

Open AccessReview

Second-Opinion Systems for Rare Diseases: A Scoping Review of Digital Workflows and Networks

by Vinícius Lima, Mariana Mozini and Domingos Alves

Informatics 2026, 13(1), 6; https://doi.org/10.3390/informatics13010006 - 10 Jan 2026

Viewed by 316

Abstract

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe [...] Read more.

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe how second-opinion services for rare diseases are organized and governed, to characterize technological and workflow models, to summarize benefits and barriers, and to identify priority evidence gaps for implementation. Methods: Using a population–concept–context approach, we included peer-reviewed studies describing implemented second-opinion systems for rare diseases and excluded isolated case reports, purely conceptual proposals, and work outside this focus. Searches in August 2025 covered PubMed/MEDLINE, Scopus, Web of Science Core Collection, Cochrane Library, IEEE Xplore, ACM Digital Library, and LILACS without date limits and were restricted to English, Portuguese, or Spanish. Two reviewers screened independently, and the data were charted with a standardized, piloted form. No formal critical appraisal was undertaken, and the synthesis was descriptive. Results: Initiatives were clustered by scale (European networks, national programs, regional systems, international collaborations) and favored hybrid models over asynchronous and synchronous ones. Across settings, services shared reproducible workflows and provided faster access to expertise, quicker decision-making, and more frequent clarification of care plans. These improvements were enabled by transparent governance and dedicated support but were constrained by platform complexity, the effort required to assemble panels, uneven incentives, interoperability gaps, and medico-legal uncertainty. Conclusions: Systematized second-opinion services for rare diseases are feasible and clinically relevant. Progress hinges on usability, aligned incentives, and pragmatic interoperability, advancing from registries toward bidirectional electronic health record connections, alongside prospective evaluations of outcomes, equity, experience, effectiveness, and costs. Full article

(This article belongs to the Section Health Informatics)

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15 pages, 5247 KB

Open AccessArticle

Visual Harmony Between Avatar Appearance and On-Avatar Text: Effects on Self-Expression Fit and Interpersonal Perception in Social VR

by Yang Guang, Sho Sakurai, Takuya Nojima and Koichi Hirota

Informatics 2026, 13(1), 5; https://doi.org/10.3390/informatics13010005 - 7 Jan 2026

Viewed by 332

Abstract

In social virtual reality (VR) and metaverse platforms, users express their identity through both avatar appearance and on-avatar textual cues, such as speech balloons. However, little is known about how the harmony between these cues influences self-representation and social impressions. We propose that [...] Read more.

In social virtual reality (VR) and metaverse platforms, users express their identity through both avatar appearance and on-avatar textual cues, such as speech balloons. However, little is known about how the harmony between these cues influences self-representation and social impressions. We propose that when avatar appearance and text design, including color, font, and tone, are consistent, users experience a stronger self-expression fit and elicit greater interpersonal affinity. A within-subject study (

N = 21

) in VRChat manipulated the social context, color harmony between avatar hair and text, and style or content consistency between tone and font. Questionnaires provided composite indices for perceived congruence, self-expression fit, and affinity. Analyses included repeated-measures ANOVA, linear mixed-effects models, and mediation tests. Results showed that congruent pairings increased both self-expression fit and affinity compared to mismatches, with mediation analyses indicating that self-expression fit fully mediated the effect. These findings integrate theories of avatar influence and computer-mediated communication into a framework for metaverse design, highlighting the value of consistent avatar and text styling. Full article

(This article belongs to the Section Human-Computer Interaction)

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26 pages, 3117 KB

Open AccessArticle

C-STEER: A Dynamic Sentiment-Aware Framework for Fake News Detection with Lifecycle Emotional Evolution

by Ziyi Zhen and Ying Li

Informatics 2026, 13(1), 4; https://doi.org/10.3390/informatics13010004 - 5 Jan 2026

Viewed by 430

Abstract

The dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic [...] Read more.

The dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic affective cues. Many approaches focus on static text or propagation topology, limiting their robustness and failing to model the complete emotional life-cycle for applications such as assessing veracity. This paper introduces C-STEER (Cycle-aware Sentiment-Temporal Emotion Evolution), a novel framework grounded in communication theory, designed to model the characteristic initiation, burst, and decay stages of these emotional arcs. Guided by Diffusion of Innovations Theory, C-STEER first segments an information cascade into its life-cycle phases. It then operationalizes insights from Uses and Gratifications Theory and Emotional Contagion Theory to extract stage-specific emotional features and model their temporal dependencies using a Bidirectional Long Short-Term Memory (BiLSTM). To validate the framework’s descriptive and predictive power, we apply it to the challenging domain of fake news detection. Experiments on the Weibo21 and Twitter16 datasets demonstrate that modeling life-cycle emotion dynamics significantly improves detection performance, achieving F1-macro scores of 91.6% and 90.1%, respectively, outperforming state-of-the-art baselines by margins of 1.6% to 2.4%. This work validates the C-STEER framework as an effective approach for the computational modeling of collective emotion life-cycles. Full article

(This article belongs to the Special Issue Practical Applications of Sentiment Analysis)

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24 pages, 2422 KB

Open AccessArticle

A Clustering Approach to Identify Risk Perception on Social Networks: A Study of Peruvian Children and Adolescents

by Yasiel Pérez Vera, Richart Smith Escobedo Quispe and Patrick Andrés Ramírez Santos

Informatics 2026, 13(1), 3; https://doi.org/10.3390/informatics13010003 - 4 Jan 2026

Viewed by 826

Abstract

The excessive and inappropriate use of the internet by children and young people increases their exposure to risky situations, especially since the COVID-19 pandemic. This study analyzes risky situations on social media among children and adolescents. The objective of this work was to [...] Read more.

The excessive and inappropriate use of the internet by children and young people increases their exposure to risky situations, especially since the COVID-19 pandemic. This study analyzes risky situations on social media among children and adolescents. The objective of this work was to identify the risks associated with the use of social media. A comparative analysis of five clustering algorithms was applied to a dataset developed by eBiz Latin America in collaboration with La Salle University of Arequipa and the Institute of Christian Schools of the De La Salle Brothers of the Bolivia-Peru district. Among the results, it was shown that children around 11 years old display a high prevalence of digital risk behaviors such as adding strangers, followed by pretending to be someone else; adults around 43 years old exhibit a tendency to follow strangers and, even more so, to take photographs without permission; adolescents with an average age of 11 show a heavy use of YouTube, TikTok, and Instagram. It is concluded that among digital risks in children and adults, the clusters highlight shared vulnerabilities, such as the addition of strangers and exposure to requests for personal data, which persist throughout the life stages but intensify in early adulthood. These findings emphasize the urgency of preventive policies addressing generational differences in social network use to promote proactive responses to digital harassment. Full article

(This article belongs to the Section Machine Learning)

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13 pages, 1561 KB

Open AccessArticle

AIMarkerFinder: AI-Assisted Marker Discovery Based on an Integrated Approach of Autoencoders and Kolmogorov–Arnold Networks

by Pavel S. Demenkov, Timofey V. Ivanisenko and Vladimir A. Ivanisenko

Informatics 2026, 13(1), 2; https://doi.org/10.3390/informatics13010002 - 24 Dec 2025

Viewed by 401

Abstract

In modern bioinformatics, the analysis of high-dimensional data (genomic, metabolomic, etc.) remains a critical challenge due to the “curse of dimensionality,” where feature redundancy reduces classification efficiency and model interpretability. This study introduces a novel method, AIMarkerFinder (v0.1.0), for analyzing metabolomic data to [...] Read more.

In modern bioinformatics, the analysis of high-dimensional data (genomic, metabolomic, etc.) remains a critical challenge due to the “curse of dimensionality,” where feature redundancy reduces classification efficiency and model interpretability. This study introduces a novel method, AIMarkerFinder (v0.1.0), for analyzing metabolomic data to identify key biomarkers. The method is based on a denoising autoencoder with an attention mechanism (DAE), enabling the extraction of informative features and the elimination of redundancy. Experiments on glioblastoma and adjacent tissue metabolomic data demonstrated that AIMarkerFinder reduces dimensionality from 446 to 4 key features while improving classification accuracy. Using the selected metabolites (Malonyl-CoA, Glycerophosphocholine, SM(d18:1/22:0 OH), GC(18:1/24:1)), the Random Forest and Kolmogorov–Arnold Networks (KAN) models achieved accuracies of 0.904 and 0.937, respectively. The analytical formulas derived by the KAN provide model interpretability, which is critical for biomedical research. The proposed approach is applicable to genomics, transcriptomics, proteomics, and the study of exogenous factors on biological processes. The study’s results open new prospects for personalized medicine and early disease diagnosis. Full article

(This article belongs to the Section Machine Learning)

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27 pages, 954 KB

Open AccessArticle

SAFE-GUARD: Semantic Access Control Framework Employing Generative User Assessment and Rule Decisions

by Nastaran Farhadighalati, Luis A. Estrada-Jimenez, Sepideh Kalateh, Sanaz Nikghadam-Hojjati and Jose Barata

Informatics 2026, 13(1), 1; https://doi.org/10.3390/informatics13010001 - 19 Dec 2025

Viewed by 572

Abstract

Healthcare faces a critical challenge: protecting sensitive medical data while enabling necessary clinical access. Evolving user behaviors, dynamic clinical contexts, and strict regulatory requirements demand adaptive access control mechanisms. Despite strict regulations, healthcare remains the most breached industry, consistently facing severe security risks [...] Read more.

Healthcare faces a critical challenge: protecting sensitive medical data while enabling necessary clinical access. Evolving user behaviors, dynamic clinical contexts, and strict regulatory requirements demand adaptive access control mechanisms. Despite strict regulations, healthcare remains the most breached industry, consistently facing severe security risks related to unauthorized access. Traditional access control models cannot handle contextual variations, detect credential compromise, or provide transparent decision rationales. To address this, SAFE-GUARD (Semantic Access Control Framework Employing Generative User Assessment and Rule Decisions) is proposed as a two-layer framework that combines behavioral analysis with policy enforcement. The Behavioral Analysis Layer uses Retrieval-Augmented Generation (RAG) to detect contextual anomalies by comparing current requests against historical patterns. The Rule-Based Policy Evaluation Layer independently validates organizational procedures and regulatory requirements. Access is granted only when behavioral consistency and both organizational and regulatory policies are satisfied. We evaluate SAFE-GUARD using simulated healthcare scenarios with three LLMs (GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Flash) achieving an anomaly detection accuracy of 95.2%, 94.1%, and 91.3%, respectively. The framework effectively identifies both compromised credentials and insider misuse by detecting deviations from established behavioral patterns, significantly outperforming conventional RBAC and ABAC approaches that rely solely on static rules. Full article

(This article belongs to the Special Issue Health Data Management in the Age of AI)

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Informatics, Volume 13, Issue 1 (January 2026) – 17 articles

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