Search Results (529)

Early identification of depression risk from social media text can support large-scale screening and timely follow-up. However, posts are often emotionally complex and linguistically ambiguous, which makes robust detection challenging. This paper proposes RST-DS (Rhetorical Structure Theory-based Depression Scanning), a discourse-aware framework that integrates Rhetorical Structure Theory (RST) signals with lexical evidence for classifying posts as Depressed/Non-Depressed (D/ND). Using Reddit posts, we compute an RST-derived score capturing rhetorical relationships and coherence and fuse it with lexical features represented via Term Frequency–Inverse Document Frequency using two strategies: feature addition (+) and feature concatenation ($\left|\right|$). We evaluate K-Nearest Neighbors (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), along with a soft-voting ensemble model named MLPBoostReg (an ensemble of LR, XGBoost, and MLP). Using a 5-fold cross validation, the concatenation strategy consistently outperforms the addition strategy across accuracy, precision, recall, and F1-score. The best-performing configuration, MLPBoostReg$\left|\right|$ achieves the highest accuracy, precision, recall, and F1-score of 0.970, indicating a strong balance between identifying depression-related content and limiting false alarms. These findings suggest that combining rhetorical structure with lexical evidence improves depression detection from social media text, while noting that social-media labels do not substitute for clinical diagnosis. Full article

Effective sustainable environmental governance requires feedback between institutional risk management and lived public experience. We propose a hierarchical Topic–Aspect–Frame framework to compare pollution discourse across news and social media. Using 30,635 chunk-level Taiwanese Chinese texts collected from OpView Product Insight in May 2026, treated as an early summer one-month validation window, we analyze topic selection, aspect foregrounding, and frame-based risk construction. Category-balanced audits yielded conditional label precision among analyzable chunks of 91.3% for topics, 85.8% for aspects, and 90.1% for frames. Among 30,304 frame-eligible chunks, Governance/Regulation was more prevalent in news (gap = +4.47 pp; 95% CI = [+3.11, +5.88]), whereas Health and Lived Risk was more prevalent in social media (gap = −9.84 pp; 95% CI = [−10.68, −9.02]). Under the aspect-conditioned prototype design, sequential decomposition attributed the latter gap mainly to topic selection (−5.40 pp) and aspect foregrounding (−4.14 pp), with a smaller within-aspect component (−0.30 pp). Both core directions persisted across four fixed windows and 100 same-fraction subsamples. The framework offers a preliminary, auditable approach for identifying cross-media governance-experience mismatches, subject to seasonal, platform, denominator, and model-support constraints. Full article

Automatic detection of stress in social media text holds promise for supporting digital mental health, but most existing Transformer-based approaches are opaque and computationally demanding. This work presents DR-Transformer, a Dual-Regularized Transformer that combines two complementary mechanisms: (i) a group sparsity penalty ($L_{2,1}/L_2$ elastic net) applied to the query and key projection matrices of every attention head, which encourages whole-row sparsity, producing more concentrated and inspectable attention patterns; (ii) a supervised contrastive loss on the [CLS] projection, which organizes the latent space according to the stress label. The architecture is intentionally lightweight (six layers, eight heads, 256-dim embeddings; ∼9.5 M parameters) and runs entirely on consumer-grade hardware (NVIDIA GTX 1660, 6 GB). Experiments on the publicly available Dreaddit dataset (binary stress classification, 2838 train/715 test segments) compare DR-Transformer against Logistic Regression, BiLSTM, a Standard Transformer of identical architecture, and MentalBERT. Across five seeded runs, DR-Transformer (Full) reaches $F_1=0.876$ (bootstrap 95% CI $0.852$–$0.898$), outperforming the Standard Transformer ($F_1=0.842$; McNemar $p<0.001$ with Bonferroni correction) and performing comparably to the much larger MentalBERT ($F_1=0.879$; $p=0.421$). Sparse regularization increases the fraction of near-zero attention weights (below $0.01$) from $0.215$ to $0.682$, while the supervised contrastive loss improves the silhouette score of [CLS] embeddings from $0.312$ to $0.483$. Dual regularization thus combines accuracy, efficiency, and structurally induced attention concentration in a single model which can be trained without specialized infrastructure. We use the term “interpretable” throughout in this restricted, structural sense—to refer to concentrated and inspectable attention—rather than in the sense of established causal or mechanistic faithfulness; this is only partially and indirectly supported by our token deletion analysis. Full article

Recent generative art outputs across music, literature, painting and moving-image media have attracted extensive scholarly and public interest, yet evaluations of their creative capacities are mostly limited to informal observational accounts. Drawing on neuroaesthetic reasoning, this paper puts forward a dual-layer analytical framework that differentiates two distinct modes of aesthetic reception: Alignment, defined as statistical template matching, and evocation, referring to the novel association of scattered embodied memory fragments. Building on this binary categorization, the study introduces the tentative Ring Scale taxonomy—a figurative target-shooting metaphor rather than quantitative metric—as a purely descriptive tool for stratifying relative aesthetic evocation intensity. This framework further unpacks the neurocognitive underpinnings of auditory, visual and textual aesthetic pathways, alongside their combined multimodal interactions within film and television works. It tentatively accounts for why generative systems tend to deliver more cohesive aesthetic outcomes within the auditory domain, and hypothesises a present functional limitation of current large models: these systems perform comparatively well within Alignment-driven aesthetic effects, while layered high-order evocation remains constrained by inherent structural limitations of statistical training architectures. From this diagnostic observation, three directional paradigm shifts for human–AI collaborative creation are outlined: shifting from human substitution to human–machine complementarity, shifting from exhaustive template imagery generation to targeted latent fragment elicitation, and shifting from optimising figurative Ring-tier descriptive labels to pursuing transformative aesthetic fission effects. The study frames imaginative cognition as the central driving force behind fruitful human–AI co-creation, and positions prompt engineering as the actionable operational bridge connecting human imaginative thought to machine-executable generative parameters. Three tentative prompt design tactics are then elaborated: physiological arousal framing, multisensory scenario simulation prompts, and intentional strategic blank-leaving. Additionally, this work discusses the plausible constructive functions of model hallucination phenomena when viewed through the lens of high-tier aesthetic evocation, rather than merely framing such outputs as technical errors. All judgments and tier comparisons raised throughout the paper are framed as unvalidated observational hypotheses open to empirical testing. To facilitate follow-up empirical scrutiny, the paper collates a full set of testable hypotheses derived from its theoretical reasoning and outlines feasible experimental validation pipelines, with an open call for controlled empirical research to corroborate or refine the proposed qualitative framework. Full article

Coordinated and bot-like activity on social media is usually studied with supervised detectors that need rich account data such as profiles, timelines, and follower networks, which is increasingly hard to obtain. We ask what can be established about a single account’s reply ecosystem from its publicly visible posts and replies alone, with no profiles, timelines, or follower data. In a case study of the reply ecosystem of an official political party account (23,953 replies by 1985 accounts, December 2025 to January 2026), we compute profile-free behavioral features covering text duplication, character-level entropy, timing regularity, reply latency, and post coverage, complemented by a co-commenting network analysis, and group active accounts with unsupervised density-based clustering. The clustering, combined with two transparent labeling rules, separates three behavioral tiers: templated amplifiers defined by text reuse, persistent responders with human-like text but extreme volume and coverage, and an organic remainder. The two non-organic tiers comprise 5.4% of accounts, yet produce 53.5% of all comments, a composition that is stable under resampling and threshold sensitivity analysis, with a failure mode that is only conservative, since over-strict settings leave a tier unassigned rather than reshaping it. The platform’s own spam flags, never used as input, rise steadily from organic accounts to templated amplifiers, consistent with the behavioral grouping. Full article

Background: Social-media-based risk screening is promising, yet deployment quality is constrained by noisy language, class imbalance, low explainability, and uncertainty handling. Objective: This work develops an application-specific BiLSTM-centered proxy risk screening framework that combines chaos-regularized learning, SMOTE balancing, SHAP explanation, and TF-IDF similarity fallback. Methods: Experiments used an annotated Twitter corpus (>31,000 English posts) with neutral, hate, and offensive labels, where hate/offensive content was treated only as a proxy risk indicator rather than as evidence of depression, self-harm, or clinical psychological distress. The workflow covered text normalization, tokenization, fixed-length sequence modeling, imbalance correction, and thresholded inference. The model stack used GloVe embeddings with recurrent layers and chaos-based dropout, trained with Adam plus adaptive stopping/scheduling. Operational safety was supported through lexicon checks and a balanced TF-IDF reference set for low-confidence cases. Results: Validation performance reached an F1-score of 0.86, accuracy 0.79, and AUC 0.93 under the reported pipeline. Component-level evidence further indicated that SHAP, thresholding, TF-IDF retrieval, and lexicon checks add explanation, uncertainty handling, reference context, and conservative escalation beyond the underlying classifier, although their marginal effects were not isolated through rerun ablation. Conclusion: The proposed system offers a practical tradeoff between predictive quality, interpretability, and safety-aware operation for proxy risk screening on noisy social media. The contribution is positioned as an integrated application pipeline rather than a new NLP algorithm, and future work should validate the framework with clinically grounded labels, leakage-safe resampling, ablation studies, and transformer baselines. Full article

Roman Urdu hate and offensive language detection is difficult because informal social-media posts combine non-standard spelling, code-mixing, local abusive expressions, and severe label imbalance. Existing transformer-based detectors improve contextual modeling, but they often leave lexical threat evidence implicit and rarely address whether model confidence is reliable when large language model teacher signals are used in low-resource training. We propose Dynamic Gated Distillation with Symbolic Evidence (DGDS), a reliability-gated neuro-symbolic learning framework for Roman Urdu hate and offensive language detection. DGDS combines a cross-lingual semantic encoder, a context-modulated symbolic threat profiler, and a reliability-gated dual-stream learner. The key idea is to treat the large language model teacher output as a noisy structured soft-label record rather than as a replacement for gold labels. DGDS therefore uses the teacher probability assigned to the gold class, the current student confidence, and a teacher reliability gate to form a dynamic importance weight for supervised training. Under a leakage-aware evaluation protocol on the RUHSOLD benchmark, the central contribution of DGDS is confidence reliability: it holds macro-F1 at parity with the strongest dual-stream baseline on both the binary and five-class settings while substantially reducing the expected calibration error from 0.0512 to 0.0134 on Task 1 (a 74% relative reduction) and from 0.0813 to 0.0268 on Task 2 (a 67% relative reduction). These calibration gains are statistically significant ($p<0.001$, paired stratified bootstrap) and robust to teacher replacement (GPT-4o-mini yields ECE 0.0158 vs. the 0.0512 baseline on Task 1). These results support a conservative conclusion: large language model soft labels are most useful for Roman Urdu harmful-content detection when they are filtered as uncertain training evidence and deployed through a local calibrated student model. Full article

This study examines how the chromatic characteristics of the Min River urban waterfront are represented in publicly circulated social media images to support place-based biophilic landscape design. Dominant-color records were extracted through pixel-level filtering and per-image K-means clustering in CIELAB space, with image-level lighting-condition interpretation and record-level landscape-element labeling assisted by a multimodal large language model and subsequently reviewed and corrected by the author. Hue distributions and five record-proportion-weighted metrics of saturation, value, vividness, chromatic dissonance, and complexity were examined through an analytical framework integrating temporal scenarios, landscape elements, and lighting conditions. The results reveal a recurrent blue–orange orientation produced by the complementary positioning of multiple landscape elements rather than by any single category. Nighttime imagery showed the highest saturation but the lowest value, whereas dawn and dusk combined relatively high saturation and value and produced the highest vividness and chromatic dissonance. Transitional illumination brought built surfaces closer to natural elements in saturation–value space, while Ward hierarchical clustering identified Color-Affinity Groups that crossed temporal, lighting-condition, and landscape-element boundaries. These findings support a relational interpretation of biophilic color as a condition-dependent configuration rather than a fixed set of element-bound hues. Weighted representative palettes provide scenario-sensitive design references. However, the findings characterize publicly circulated visual representations of the waterfront rather than calibrated physical-color measurements or direct evidence of restorative effects. Full article

Fake social media profiles increasingly resemble ordinary accounts, combining believable images, fluent biographies, copied posts, and selective engagement, which makes single signal detection unreliable. This study develops and evaluates a forensic natural language processing (NLP) framework that treats profile text as digital evidence rather than raw model input. Profiles were collected from Facebook, Instagram, X, Truth Social, and LinkedIn through network expansion and vocabulary guided scraping, then anonymized, merged, cleaned, and organized into structured text and profile records. Ground truth came from human eye annotation (referred to throughout as human eye review, human review, or manual review) supported by three LLM reviewers (referred to throughout as LLM assisted review), OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini 1.5 Pro, with strict agreement logic producing confidence-tiered labels across platform-specific label spaces covering scam, fraud, harmful, and legitimate behavior. Each model received a defined set of label categories, was instructed to return a single label with a one sentence rationale, and worked independently with no cross-model communication. Multidimensional features, including TF-IDF representations, sentence embeddings, scam lexicon counts, sentiment and emotion scores, and behavioral indicators, were extracted through a pipeline built to keep links, hashtags, contact markers, and repeated phrasing as forensic signals rather than noise. Traditional classifiers, transformer models, and ensembles were trained on verified labels, and the best model per platform was applied to the full dataset of over 309,108 records under confidence filtered classification, with predictions below a 0.70 probability threshold flagged for human review. The framework links classification outputs to textual indicators through explainable AI, achieving weighted F1 scores between 0.86 and 0.96 across five platforms. These results show that profile text alone provides strong, production-relevant evidence for detecting fake and harmful accounts, even though the framework treats this as one evidence stream to be weighed alongside image and behavioral signals rather than as a final determination on its own. The accompanying evidence trail means an investigator or a trust and safety team can see why a profile was flagged rather than being handed a bare score, which is what turns a classifier into a tool that can be checked, challenged, and used directly in an investigation. Full article

Non-covalent forces are the primary drivers of biomolecular interactions. They also represent a key factor in the unexpected tendency for proteins, peptides, and even individual amino acids to self-assemble into precise supramolecular assemblies, often characterized by a β-rich, amyloid-like structure. Studies carried out in the last few decades have shown that peptides/proteins self-assembly not only has structural consequences but also generates spectroscopic properties whose origin remains debated. Here, we investigated the spectroscopic properties of four peptides, GAIIGL, NNQQ, SSTSAA, and GNNQQNG (a derivative of the frequently studied GNNQQNY peptide), devoid of aromatic residues, whose crystallographic characterization has been seminal in elucidating the basis of the aggregation process. The spectroscopic behavior of these peptides was analyzed in two different media and in the solid state, highlighting the conditions that favor UV–visible fluorescence emission. These investigations have also prompted the use of this fluorescence as a potential diagnostic tool in nanomedicine. Importantly, the identification of intrinsic fluorescence signatures in these amyloid-like assemblies may support the development of label-free optical approaches for the early detection and monitoring of aggregation-related pathological processes. For all peptides, the emitted fluorescence spans a rather wide range of wavelengths. Although centered in the blue region, a significant signal is also observed in the green region, independent of their physical state. The structural analysis of the solid used to collect the spectroscopic data reveals features related to the packings in the crystal state. This observation suggests that their three-dimensional crystal structures may serve as reliable models for studies aimed at correlating structural and spectroscopic features. Full article

Prolonged short-video engagement in the generative-AI era may be shaped by interface cues that encourage or interrupt repeated continuation decisions in algorithmic feeds. This study examines whether AI-generated content disclosure functions as interface-level digital friction for prolonged short-video engagement among late-adolescent and emerging-adult TikTok users. Prolonged watching intention is treated as a cognitive-behavioral proximal tendency relevant to problematic social media use (PSMU), rather than as a clinical diagnosis or an emotional-disturbance outcome. Drawing on the heuristic-systematic model, we tested a dual-pathway risk-trust model in which disclosure directly affects prolonged watching intention, while perceived risk and content trust operate as mediators and AI literacy operates as a person-level boundary condition. An online between-subjects experiment was conducted with 720 valid participants aged 18–24. Disclosure had a positive direct effect on prolonged watching intention, suggesting that AI labels can initially work as salient curiosity and novelty cues. At the same time, disclosure increased perceived risk and reduced content trust, generating negative indirect pathways that constrained prolonged watching intention. AI literacy strengthened both appraisal pathways. The findings reposition AI disclosure from a mere transparency notice to a behavioral cue that can simultaneously attract attention and activate protective appraisal. They contribute to developmental and media-psychological research on prolonged engagement and PSMU-relevant mechanisms without overstating clinical implications. Full article

Adaptive bitrate streaming over HTTP relies on carefully constructed bitrate ladders and ordered sets of bitrate–resolution pairs to deliver optimal perceptual quality under fluctuating network conditions. While content-aware methods based on convex hull optimisation have substantially improved ladder efficiency for Video-on-Demand, they require exhaustive multi-resolution pre-encoding that is computationally prohibitive under the real-time constraints of live streaming. This challenge is compounded by the H.266/Versatile Video Coding (VVC) standard, which offers approximately 50% compression gains over HEVC at 8–10× the encoding complexity. This paper presents LiveCH-VVC, a latency-aware dynamic bitrate ladder prediction framework for VVC-encoded live streaming over Low-Latency DASH (LL-DASH) with CMAF packaging. The framework introduces four integrated modules: (i) a Lightweight Dual-Path CNN (LDP-CNN), obtained via teacher–student knowledge distillation (∼5 M parameters, 148 ms GPU inference), that jointly extracts spatial–temporal features from raw frames and compression-domain statistics from a fast VVC probe encode; (ii) an adaptive scene change detector with exponential moving average thresholding (F1 = 0.925) that triggers ladder updates only upon significant complexity shifts; (iii) a temporally augmented XGBoost multi-label classifier that predicts latency-constrained Pareto-optimal bitrate–resolution pairs; and (iv) an online adaptation engine that integrates Common Media Client Data (CMCD) feedback from CDN edge servers for continuous closed-loop refinement. Comprehensive evaluation on 81 UHD sequences (∼4050 CMAF segments) from three benchmark datasets demonstrates an average BD-Rate of $+0.68\%$ relative to the per-segment oracle convex hull $5.4\times$ better than the state-of-the-art ARTEMIS framework ($+3.67\%$) while achieving 73.3% encoding time savings, 2.37 s end-to-end latency, and a QoE score of 81.6 in live simulation with 100 concurrent clients. Ablation analysis confirms that the dual-path compression-domain branch ($+0.44$ pp) and temporal context augmentation ($+0.35$ pp) are the primary performance drivers, while the online adaptation mechanism provides $42\%$ relative improvement over extended streaming sessions. Full article

Arabic multi-label emotion detection (MLED) in social media remains challenging because dialectal variation, implicit affective cues, and polarity-opposed emotions may occur within the same post. Existing Arabic MLED studies have mainly emphasized thresholded predictive performance, with limited attention to whether model confidence remains reliable under emotionally conflicting conditions. In this study, we propose CONCORD-Emo (CONflict-aware Compositional Representation for Emotion Detection), a reliability-aware framework for Arabic MLED. The framework adopts established label-wise attention, mixture-of-experts routing, Monte Carlo (MC) dropout, and post hoc temperature scaling as supporting mechanisms, while its architecture-level contribution is the conflict-conditioned integration of a residual global anchor with a conflict-aware fusion gate supervised by an automatically derived polarity-conflict target. We evaluated the framework on three Arabic benchmarks: SemEval-2018-Ar, ExaAEC, and SemEval-2025-Arq using predictive and reliability-oriented criteria. CONCORD-Emo remains competitive with strong MARBERT-based baselines. On SemEval-2025-Arq, it attains point estimates of 0.471 for Jaccard, 0.606 for micro-F1, and 0.582 for macro-F1. Paired bootstrap confidence intervals show that most predictive differences include zero, whereas the lower Expected Calibration Error and Brier scores on SemEval-2018-Ar and ExaAEC are consistently supported relative to the controlled baselines. Conflict-conditioned analysis shows that polarity-conflict instances yield lower predictive performance and higher Brier scores than blended-emotion instances. Taken together, these results support a reliability-aware evaluation of Arabic MLED in which polarity conflict, calibration, uncertainty estimation, and selective prediction are examined alongside predictive performance. Full article

Introduction: Heavy metal contamination in water represents a major environmental and public health challenge because toxic ions frequently occur as complex multi-species mixtures rather than isolated pollutants. This study presents a numerical design and optimization of a surface plasmon resonance (SPR) sensor based on a LaF/Au/BaTiO₃/WS₂ heterostructure for monitoring refractive-index changes associated with mixed heavy metal ions in aqueous media. Methodology: The optical response of the multilayer sensor was evaluated using the transfer matrix method under TM-polarized illumination at 633 nm. Systematic optimization was performed for the prism substrate, Au thickness, dielectric oxide layer, and 2D nanomaterial interface. The final configuration consisted of a LaF prism, 50 nm Au film, 2.0 nm BaTiO₃ spacer, and 0.80 nm WS₂ monolayer. Sensor performance was assessed using resonance-angle shift, sensitivity, detection accuracy, quality factor, figure of merit, FWHM, attenuation, and estimated limit of detection. Results and Discussion: The optimized LaF/Au/BaTiO₃/WS₂ configuration produced stable simulated SPR responses across single, binary, quaternary, and five-ion heavy metal matrices. The WS₂ monolayer provided the highest angular displacement among the evaluated 2D materials, while BaTiO₃ improved field confinement and limited optical damping in the numerical model. The configuration maintained attenuation near 1.6%, FWHM values around 7.9°, detection accuracy between 0.030 and 0.032 deg⁻¹, and model-based refractometric LoD values down to 3.49 × 10⁻⁵ RIU under the assumed angular-resolution criterion. Conclusions: The proposed LaF/Au/BaTiO₃/WS₂ SPR configuration provides a numerical framework for label-free monitoring of refractive-index changes associated with complex heavy-metal-ion mixtures in contaminated water. Experimental fabrication and testing are required to validate the simulated performance. Full article

Deep saline aquifers are among the most promising formations for long-term geological CO₂ storage due to their extensive distribution and large storage capacity. Accurate estimation of the CO₂ diffusion coefficient in brine is essential for modeling dissolution trapping, one of the most long-term reliable CO₂ sequestration mechanisms. However, laboratory measurements under reservoir conditions are costly and time-intensive, motivating the development of efficient predictive tools. This study develops machine learning (ML) frameworks for predicting bulk and porous media CO₂ diffusivity by combining two data augmentation strategies—Conditional Tabular Generative Adversarial Networks (CTGAN) and pseudo-labeling (PL)—with four ML algorithms: Random Forest Regression (RFR), XGBoost Regression (XGBR), Natural Gradient Boosting (NGBoost), and Gene Expression Programming (GEP). The database consists of 186 bulk diffusivity and 47 porous media diffusivity observations compiled from experimental and molecular dynamics studies, covering pressures of 0.1–30 MPa, temperatures of 286–673 K, salinities up to 300,000 ppm, and permeabilities of 0.05–2500 Darcy. Data augmentation increased dataset density by approximately 40%, resulting in hybrid datasets of 260 and 68 samples for bulk and porous media diffusivity, respectively. Results show that PL consistently outperforms CTGAN augmentation by preserving physically meaningful relationships and improving predictive accuracy. NGBoost achieved the best performance, with RMSE values of 0.33 and 0.61 for bulk and porous media diffusivity, respectively. Feature-importance analysis identified temperature as the dominant control on diffusivity, followed by pressure and salinity, while permeability exhibited limited influence. The developed framework provides a computationally efficient alternative to extensive laboratory measurements and offers a reliable tool for reservoir simulation, CO₂-EOR studies, and geological carbon storage design under data-limited conditions. Full article