Search Results (2,190)

The integration of artificial intelligence (AI) into online education has transformed the digital learning space, offering new ways to enhance learner satisfaction and engagement. This systematic literature review, covering a five-year span from 2020 to 2025, explores how AI technologies, such as chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking and predictive analytics, support learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, the current review addresses three central research questions: (1) What aspects of AI positively influence learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges from using these technologies may arise; and (3) What research approaches are most commonly used to assess AI’s impact in such learning contexts? The findings highlight that adaptive learning, real-time feedback, and emotion-aware systems contribute positively to personalized learning and motivation. However, concerns persist around data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, as well as machine learning, mixed methods, and survey-based approaches are found to dominate in reviewed studies. Based on these insights, this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains. Full article

Background: Suicidality continues to rise, while mental health services face obstacles of access, availability, and affordability. Digital peer support (DPS) may help bridge these gaps and facilitate early identification of suicidal ideation (SI). Objective: This study examined (1) the effectiveness of a hybrid solution combining a proprietary AI-based SI detection with real-time human moderation within DPS, (2) distribution of SI, (3) active SI referral, (4) linguistic differences in SI, (5) sentiment changes among users, and (6) the effects of peer SI disclosure. Methods: We retrospectively analyzed 169,181 live-chat transcripts encompassing 449,946 user visits (January–December 2024) from a DPS provider, Supportiv. Passive and active SI were identified using a hybrid AI and human moderator solution with post hoc LLM verification. Sentiment analysis and ANCOVA compared changes in sentiment across three propensity-matched user groups: passive SI users, non-SI users exposed to peer SI, and non-SI users not exposed to SI. Results: SI occurred in 3.19% of live chats. The AI model identified SI faster than humans (in 77.52% passive and 81.26% active cases), with 90.3% agreement. Moderators followed up 71.3 s after AI alerts and referred 5472 active SI users (1.21%) to crisis care. All users significantly benefited from DPS, with reductions up to 29.3% in depression, 26.8% in loneliness, 25.3% in despair, and 22.3% in helplessness, with optimism increasing up to 40.4%. Conclusions: AI-integrated, human-moderated DPS offers scalable and effective support for high-risk populations. The proprietary SI detection AI model accurately detects suicidality, allowing for human-moderated DPS to improve the mental well-being of users with and without SI, and maintains peer safety. Full article

This study investigates the emotional tone in international news coverage of Saudi women’s empowerment, with a focus on their recruitment into the military as a milestone reform. The analysis is based on 22 news articles published between 2018 and 2023 across Western, regional Saudi and Arab, and non-Western international media outlets, including coverage from Asian media contexts such as China and India. Drawing on sentiment analysis; the study employed lexicon-based tools (LIWC; Bing; and AFINN) alongside thematic analysis using Speak AI to capture both polarity and narrative framing. This triangulated approach addressed the limitations of word-level sentiment tools by integrating contextual and thematic interpretation. The findings reveal clear regional contrasts: Western media predominantly employed negative framings, emphasizing human rights concerns and ongoing gender inequality. In contrast, regional Saudi and Arab outlets highlighted empowerment, modernization, and Vision 2030 alignment, while non-Western international outlets tended to mirror these positive narratives with limited rights-based critique. Asian media presented mixed framings. These results complicate assumptions of a simple East–West divide by showing convergence between regional and non-Western portrayals. The study contributes methodologically by demonstrating how combining polarity-based sentiment tools with thematic analysis provides a more nuanced account of media sentiment, and substantively by revealing how empowerment narratives are unevenly distributed across global media systems. Full article

Fine-tuning a BERT-Base model for specific tasks, such as sentiment analysis, has become resource-intensive and often requires high computational power and memory. This paper introduces SCALE, a novel resource-efficient fine-tuning method that targets the most critical transformer layers, which reduces computational costs without sacrificing performance. By dynamically profiling transformer layers via activation magnitudes and attention entropy, SCALE selects and adapts only the most influential layers with lightweight adapter modules. The proposed method outperforms traditional fine-tuning techniques, achieving a 2.3% improvement in accuracy on the IMDB dataset and reducing training time by 56.3% compared to full-model fine-tuning. Experiments across various sentiment analysis benchmarks demonstrate SCALE’s effectiveness in optimizing fine-tuning for the BERT-base model in resource-constrained environments, achieving up to 99% of the performance of full-model fine-tuning while using only 40% of the parameters. The empirical validation in this study is restricted to binary and multi-class sentiment classification. The evaluation specifically reflects effectiveness in sentiment analysis text classification tasks. Full article

The rise of fake reviews has become a major problem for trust in e-commerce sites. As for traditional machine learning solutions, they fail to capture the nuanced language that separates real reviews from fake reviews. In this work, we introduce a new hybrid metaheuristic algorithm that optimizes the Fusion Mamba-Attention Network (FMA-Net) for fake review detection, called GEMS (Gas-Enhanced Marine Search). GEMS is a unique combination of the exploration capabilities of the Enhanced Marine Predators Algorithm and the exploitation process of Henry Gas Solubility Optimization, offering a dual-phase optimization design for high-dimensional, asymmetric, metaheuristic-configured GEMS-optimized FMA-Net. Geometric enhancement of GEMS optimization provides GEMS-optimized FMA-Net with an accuracy of 96.8%, F1-score of 95.4%, and AUC-ROC of 97.2%, marking 3–7% improvement over the current best models for fake review detection on the Yelp, Amazon, and Google Reviews datasets. We lower the average time of hyperparameter optimization using GEMS with FMA-Net to achieve 68% reduction in overall time spent in grid search and 42% lower for complexity in comparison to genetic algorithms. The contributions of this work are the first hybrid metaheuristic for transformers, a mathematically formulated GEMS algorithm, and an extensive empirical study for proving multi-dimensional metric plausibility. Full article

Background: In 2024, Reddit, an emerging social media platform, saw a 50% increase in monthly users to nearly 100 million. Reddit has also emerged as a significant space for discussions about health conditions, including epilepsy, which affects about 50 million people globally. Purpose: This study aims to explore trends in the volume, timing, themes, emotional tone, and sentiment of posts on the r/Epilepsy subreddit from 1 December 2023 to 31 December 2024. Methods: We collected 25,222 original English-language posts from r/Epilepsy using Reddit’s Application Programming Interface (API). Data extraction was restricted to English-language submissions to ensure compatibility with sentiment and thematic analyses. We analyzed post volume and timing using chi-square tests and Poisson regression. Emotional tone was measured using TextBlob (version 0.19.0), while compound sentiment scores were calculated via VADER (Valence Aware Dictionary and Sentiment Reasoner) (NLTK version 3.9.1). A Pearson correlation assessed agreement between sentiment and emotional tone, with statistical significance set at p < 0.05. Thematic analysis was conducted using a KMeans clustering algorithm (scikit-learn version 1.6.1) to identify recurring discussion topics. Results: Total monthly posts steadily increased, with the highest number (2175) in December 2024. Peak posts in descending order were in December 2024, August 2024, and November 2024. Posts were not evenly distributed across the week, with a significant peak on Mondays (χ² = 86.75, p < 0.001) and Poisson regression confirming higher activity early in the week (p = 0.001). Emotional tones fluctuated, with positive sentiments in January and October 2024, and negative sentiments in March and August 2024. KMeans clustering identified five main themes: treatment experiences, community engagement, personal experiences, solidarity, and subreddit gratitude. Manual validation of a random subset of posts demonstrated moderate concordance between automated sentiment classification and human ratings. Conclusions: This study highlights temporal patterns, sentiment dynamics, and thematic structure in online discussions on epilepsy. Social media may offer valuable, real-time insights into patient-centered concerns and community engagement, which can inform healthcare professionals and advocacy groups in supporting individuals affected by epilepsy. Future studies may compare trends of epilepsy discussions across various social media platforms, such as X and Instagram, to further understand online patient experiences. Full article

Aligning system design with evolving user expectations remains a critical challenge in contemporary digital markets. While online reviews offer vast potential for informing product development, transforming unstructured feedback into actionable system intelligence requires rigorous analytical frameworks. This study proposes a unified framework that synthesizes topic-related text analysis, sentiment analysis, and time-series trends to model user requirements as indicators of multidimensional system value. Based on this framework, we introduce the Product Online User Perception Score to quantify user perception of product attributes through the integration of attention, discussion richness, and sentiment. Crucially, a User Requirement Value model is developed to assess the strategic priority of requirements. The model applies a discussion richness dimension to filter superficial noise and employs a reverse valuation mechanism to identify systematic gaps between high attention and low satisfaction. Comparative evidence from the Chinese automotive market highlights the evolution of user needs during the transition from fuel-powered to new energy vehicles. While manufacturers prioritize enterprise-centric intelligent features, user dissatisfaction is systematically concentrated on basic ergonomic deficits, revealing that foundational operational value remains a prerequisite for overall system success. This study shifts the analytical paradigm from descriptive monitoring to diagnostic system valuation, providing a measurable and diagnostic instrument for supporting evidence-based product iteration. Full article

The limited availability of structured and consistent health-facility information poses challenges for assessing service accessibility and quality in rapidly growing cities, particularly in the Middle East. Although digital map platforms provide extensive public data, such information is often fragmented and not directly suitable for systematic spatial analysis. This study presents GeoJed, a framework designed to automate the collection, organisation, and spatial analysis of healthcare facility information from digital map platforms. The framework is demonstrated through a case study in Jeddah, Saudi Arabia, highlighting its applicability for large-scale and reproducible spatial analysis of healthcare services. Using the resulting GeoJedHF dataset, a baseline analysis was conducted to illustrate the analytical value of the collected data, including the construction of an initial Patient Satisfaction Index (PSI) that integrates service availability with user-reported quality indicators derived from a multilingual sentiment model (XLM-RoBERTa). The results reveal clear spatial variations between districts in both facility distribution and perceived service quality. Overall, GeoJed establishes a reusable and extensible process for facility-level spatial data acquisition and analysis, with potential applications in accessibility assessment, urban planning, and service evaluation. Full article

This article examines Romanian internet memes as cultural micro-narratives that encode social critique, identity negotiation, and emotional response through compressed, multimodal storytelling. Using a mixed-method approach, the study integrates qualitative narrative analysis with quantitative sentiment data drawn from the RoMEMESv2 corpus, comprising 983 Romanian-language memes. The analysis identifies recurrent narrative roles and plot structures adapted from Propp’s morphology and applied to digital contexts, revealing archetypal roles, such as the slacker hero, the bureaucratic villain, the domestic guardian, and the trickster. From a quantitative point of view, the corpus exhibits a dominant negative sentiment, particularly within political memes, which combine systemic critique with affective ambivalence. These findings distinguish Romanian memes from datasets in other languages, suggesting that negativity functions not as deviance, but as a culturally specific narrative and emotional resource. Multimodal analysis demonstrates how visual and textual elements operate through anchorage, intertextuality, and symbolic compression, so as to construct narrative messages within single frames. The study argues that Romanian memes function as digital folklore: they narrate social frustration and institutional distrust through irony, repetition, and archetypal condensation, offering insights into the emotional and narrative logic of post-communist digital culture. Full article

The purpose of this paper is to analyze the media visibility of investments implemented in Wrocław, with a particular focus on the democratization of urban processes through the Wrocław Participatory Budget (WPB) and to study the public perception of these projects within the local information landscape. The paper presents an integrated analytical methodology combining geospatial data from the Spatial Information System of Wrocław (SIP) with textual data from the full corpus of local news articles from Wrocław. A hybrid data processing pipeline was used, including filtering of articles about Wrocław, geoparsing of location names, matching articles to investments using classic Term Frequency-Inverse Document Frequency (TF-IDF) models and embedding in language models such as HerBERT, and sentiment analysis using the XLM-T model. The results reveal strong imbalances in the visibility of WPB projects, that almost 90% of investments were not mentioned even once in the media. Temporal sentiment analysis indicated differences between categories of WPB projects. The results confirm the existence of “media deserts” and “islands of attention,” which leads to information exclusion for specific local communities and marginalized groups. This translates into asymmetry in residents’ knowledge of the real scope of the WPB program. The paper emphasizes the importance of Geographic Information System (GIS) fusion methods with natural language processing models (NLP) for urban research, and identifies directions for further analysis, including accompanying problems and limitations in the present day. Full article

Increasing community awareness of, and engagement in, biodiversity and nature are key elements in many environmental conservation strategies. However, the public may take little or no action to protect biodiversity even though they may feel a strong sense of concern about its decline. This suggests that, although members of the public may be cognitively and affectively engaged with conserving biodiversity, this engagement does not necessarily translate into behavioural engagement and support for environmental policies. We hypothesised that the association between cognitive and affective engagement with conserving biodiversity on the one hand, and conservation behaviour on the other, depends on the relevance and importance of conserving biodiversity with respect to personal needs. Using a survey of the New Zealand public (n = 1000) we found that engagement with biodiversity was associated with the personal relevance and needs-based importance of conserving biodiversity. Importantly, using conditional process analysis, we found that involvement moderates the link between cognitive and affective engagement and conservation behaviour with the link strengthening as involvement intensifies. These findings help to explain why cognitive and affective engagement with conserving biodiversity do not translate inevitably into behavioural engagement with conserving biodiversity and support for environmental policies. The implication is that, to stimulate action, knowledge and sentiment must be accompanied by the perception that action to protect biodiversity will contribute in significant ways to meeting personal needs. Full article

The article examines how framing and actor identity structure attention in short-video politics using a country-level corpus from Ecuador. It assembles 4612 public TikTok videos from official accounts and politically salient hashtags, extracts multimodal text via automatic speech recognition and on-screen OCR, and constructs two continuous indices: a quality index (programmatic, efficacy-oriented content) and a populism index (antagonistic, people-versus-elite cues). Engagement is modeled as a fractional response (binomial GLM with logit link), with robustness checks using OLS on logit(ER) and Poisson counts with an offset for log(plays + 1). Models include affect (positive sentiment and anger), hour/day controls, and actor fixed effects (leader, creator, institution, party, and media). The indices display construct validity: quality aligns with positive/joyful tone and populism with anger. Net of controls, populism is positively and consistently associated with engagement across estimators; quality is small and often null or negative. Effects are heterogeneous: leaders gain under both frames, creators primarily under populism, and media modestly under populism, while institutions face penalties under both, and parties show limited returns. Monthly series reveal event-linked intensification of populism, and hashtag networks are modular, mapping onto institutional, partisan, and creator ecosystems. A design analysis identifies a non-populist pathway—benefit-first micro-explanations, concise captions, targeted hashtags, and joyful/efficacy affect—that raises engagement without antagonism. The study contributes a reproducible, open-source pipeline for survey-free, multimodal framing measurement and clarifies how persona × frame interactions and meso-level discursive structure jointly organize attention in short-video politics. Full article

This article examines how the Global Innovation Index (GII) has become the dominant technical language for assessing and legitimizing countries’ innovation performance, and what this implies for middle-income economies such as Ecuador and Peru. We conduct a systematic review of 89 Scopus-indexed studies, combining bibliometrics with natural language processing of abstracts. The results reveal a largely optimistic discourse that frames innovation as a national, systemic construct—structured around institutions, human capital, infrastructure, market and business sophistication—while relying heavily on standardized GII metrics. Topic modeling and sentiment analysis show limited critical scrutiny of the index itself. The comparative analysis of Ecuador and Peru highlights persistent gaps between innovation inputs and outputs, with Peru leading in human capital and markets but lagging in business sophistication, and Ecuador constrained by institutional and market weaknesses. We argue that the GII should be used as a diagnostic and reform tool, not merely as a reputational ranking. Full article

Background/Objectives: The prominent role the internet plays in being a source of dental information prompts qualitative evaluation of relevant online content. This study aimed to explore patients’ experience regarding periodontal graft surgery communicated through the social media platform YouTube. Methods: An initial YouTube search using the term “gum surgery experience” retrieved 40 videos. Graft surgery was the most frequently discussed procedure, and 19 relevant videos were included in the qualitative analysis. Video content was analysed using a combined human-centered and artificial intelligence (AI)–assisted approach. AI-supported analysis of viewer comments was conducted using ChatGPT-4 and Gemini-1.5 Pro. Themes generated by human and AI analyses were compared. Results: Nine key themes were identified from the 19 videos that satisfied selection criteria. Most themes were similar between human and AI analyses, with six overlapping and three unique. The most frequently coded theme was post-operative recovery (n = 177), with pain, work absence, eating difficulties, and disrupted oral hygiene commonly reported. Patient-clinician relationships were frequently highlighted, with mixed experiences regarding communication and trust. Positive experiences were reported more frequently than negative. Comment analysis revealed varied audience engagement and sentiments, emphasizing concerns about pain, recovery, and procedural anxiety. Conclusions: Key themes related to patient experiences were identified, notably concerns regarding post-operative recovery and patient-clinician relationships. Challenges in finding information prior to having surgeries motivated patients to provide support and advice on YouTube, emphasizing the need for patient-centered resources and effective patient-clinician communication. Integrating human and AI methods in qualitative analysis was efficient and insightful, with AI supplementing but not substituting human research. Full article

The transition to environmentally sustainable healthcare is gaining urgency, yet public discourse shaping this shift remains underexamined. This study employs natural language processing (NLP) to analyze 15,976 English-language tweets (2006–2024) related to sustainable healthcare. Using Latent Dirichlet Allocation (LDA), eight dominant topics were identified, including eco-friendly access, net-zero implementation, climate impact, emissions, cost and waste, education, infrastructure, and green technologies. Sentiment analysis (VADER) of 9433 tweets showed 59.1% positive, 31.1% neutral, and 9.8% negative sentiment, with AI and technology topics receiving the highest positivity (73.5%) and climate-related topics the most negativity. Thematic analysis of 800 tweets revealed six cross-cutting themes, including healthcare’s environmental responsibility, co-benefits for health, urgency of climate action, and optimism in technological solutions. These findings offer a nuanced understanding of public perceptions, informing targeted strategies and communication for healthcare sustainability. The study also demonstrates the value of mixed-method NLP in examining enablers and barriers to health system transformation. Full article