In the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enhance personalization. However, existing models still struggle to adapt dynamically to users’ evolving interests across multiple content domains in real-time. To address this gap, the cross-domain adaptive recommendation system (CDARS) is proposed, which integrates real-time behavioral tracking with multi-domain knowledge graphs to refine user preference modeling continuously. Unlike conventional methods that rely on static or historical data, CDARS dynamically adjusts its recommendation strategies based on contextual factors such as real-time engagement, sentiment fluctuations, and implicit preference drifts. Furthermore, a novel explainable adaptive learning (EAL) module was introduced, providing transparent insights into recommendations’ evolving nature, thereby improving user trust and system interpretability. To enable such real-time adaptability, CDARS incorporates multimodal sentiment analysis of user-generated content, behavioral pattern mining (e.g., click timing, revisit frequency), and learning trajectory modeling through time-aware embeddings and incremental updates of user representations. These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. Our experimental results on datasets spanning social media, e-commerce, and entertainment domains demonstrate that CDARS significantly enhances recommendation relevance, achieving an average improvement of 7.8% in click-through rate (CTR) and 8.3% in user engagement compared to state-of-the-art models. This research presents a paradigm shift toward truly dynamic and explainable recommendation systems, creating a way for more personalized and user-centric experiences in the digital landscape.

In the digital age, climate change content on social media is frequently distorted by misinformation, driven by unrestricted content sharing and monetization incentives. This paper proposes a novel AI-based framework to evaluate the data quality of climate-related discourse across platforms like Twitter and YouTube. Data quality is defined using key dimensions of credibility, accuracy, relevance, and sentiment polarity, and a pipeline is developed using transformer-based NLP models, sentiment classifiers, and misinformation detection algorithms. The system processes user-generated content to detect sentiment drift, engagement patterns, and trustworthiness scores. Datasets were collected from three major platforms, encompassing over 1 million posts between 2018 and 2024. Evaluation metrics such as precision, recall, F1-score, and AUC were used to assess model performance. Results demonstrate a 9.2% improvement in misinformation filtering and 11.4% enhancement in content credibility detection compared to baseline models. These findings provide actionable insights for researchers, media outlets, and policymakers aiming to improve climate communication and reduce content-driven polarization on social platforms.

Climate change presents serious challenges for infrastructure, regional planning, and public awareness. However, effectively understanding and analyzing large-scale climate discussions remains difficult. Traditional methods often struggle to extract meaningful insights from unstructured data sources, such as social media discourse, making it harder to track climate-related concerns and emerging trends. To address this gap, this study applies Natural Language Processing (NLP) techniques to analyze large volumes of climate-related data. By employing supervised and weak supervision methods, climate data are efficiently labeled to enable targeted analysis of regional- and infrastructure-specific climate impacts. Furthermore, BERT-based Named Entity Recognition (NER) is utilized to identify key climate-related terms, while sentiment analysis of platforms like Twitter provides valuable insights into trends in public opinion. AI-driven visualization tools, including predictive modeling and interactive mapping, are also integrated to enhance the accessibility and usability of the analyzed data. The research findings reveal significant patterns in climate-related discussions, supporting policymakers and planners in making more informed decisions. By combining AI-powered analytics with advanced visualization, the study enhances climate impact assessment and promotes the development of sustainable, resilient infrastructure. Overall, the results demonstrate the strong potential of AI-driven climate analysis to inform policy strategies and raise public awareness.

The spread of unverified health claims about drugs, dietary supplements, and alternative remedies on social media poses a growing public health concern. In this study, we present a retrieval-augmented generation (RAG) pipeline that uses large language models (LLMs) grounded in biomedical evidence from PubMed, openFDA adverse event reports, and NIH/NCCIH dietary supplement fact sheets to detect and classify health product misinformation. A total of 3493 health-related posts were collected from Reddit (948 posts across 12 subreddits) and YouTube (2545 video descriptions and comments), from which 8250 structured claims were extracted using Claude Haiku. Each claim was matched to biomedical evidence from three authoritative sources, achieving 79.4% evidence coverage, and classified into one of five veracity categories: supported (7.0%), unsupported (59.9%), exaggerated (22.4%), contradicted (2.0%), or dangerous (8.6%), together with an associated risk tier. Overall, 13.5% of claims were assigned high or critical risk. Cross-platform analysis showed that YouTube contained higher proportions of dangerous (11.3% vs. 2.9%) and exaggerated (27.0% vs. 12.4%) claims than Reddit. Compared with keyword-based and zero-shot transformer baselines, the LLM+RAG pipeline produced a more balanced and fine-grained classification of unsupported, exaggerated, contradicted, and dangerous claims. The most frequently implicated products were ashwagandha, kratom, black seed oil, turmeric, and ivermectin, with disease cure claims showing the highest dangerous classification rate (30.1%). These model-assigned results suggest that evidence-grounded LLM pipelines can support health misinformation surveillance, while also highlighting the need for expert validation and broader cross-platform evaluation.

The rapid proliferation of synthetic and misleading media has intensified the need for robust fake media detection systems. While large language models (LLMs) have recently been employed as classifiers for misinformation detection, most existing approaches treat them as black-box predictors, overlooking their internal reasoning dynamics. In this paper, we propose a novel framework for fake media detection based on self-consistency divergence across multi-perspective LLM reasoning. Instead of generating a single verdict, the proposed method prompts an LLM to analyze a given media item from multiple independent reasoning perspectives, including factual consistency, logical coherence, emotional manipulation, and source credibility. By sampling multiple reasoning chains under controlled stochasticity, semantic divergence and logical instability across the generated explanations are quantified. We hypothesize, and empirically show, that fake media induces significantly greater reasoning variance than genuine content because fabricated narratives often lack stable factual grounding. Experiments conducted on benchmark fake news datasets show that reasoning divergence serves as a strong discriminative signal, improving detection robustness and interpretability compared to standard single-pass LLM classifiers. The findings suggest that internal reasoning instability can function as an intrinsic reliability metric, opening a new direction for explainable and model-centric fake media detection.

Smart cities collect lots of data from sources like traffic systems and sensors. This data is vital for improving services such as transport management, public safety, energy saving, and citizen involvement. However, keeping people’s privacy secure by making data anonymous can make the data less useful. Anonymization methods include mix-zones, pseudonymization, and data masking. Existing systems often evaluate the usefulness of data after it’s processed, but usually can not adjust to changes in real-time or work well across different fields. It also does not typically consider fairness for individuals or integrate well with privacy-preserving data methods on devices, which are crucial for city services needing quick responses. This paper presents a new system to assess anonymized data in smart cities in real-time. The system includes four main components: 1. A real-time tool that continuously monitors how anonymization affects data quality across various areas. 2. A utility model that adjusts usefulness scores based on the needs of different smart city services. 3. A module that detects and addresses unfair differences in data usefulness caused by anonymization. 4. A data combination layer that operates well on devices, allowing on-site decision-making while complying with privacy regulations like GDPR. The framework is tested with multiple real-world transportation datasets, anonymized in different ways, and applied to tasks like traffic prediction, anomaly detection, and location-based services. The results demonstrate that the framework provides accurate data usefulness estimation with minimal computing effort, and it offers fair, situation-specific trade-offs in usefulness in real-time. This work connects privacy and usefulness, offering a scalable solution for responsibly using anonymized data in smart city environments. The framework is validated on both transportation and smart energy datasets, demonstrating consistent improvements in privacy–utility–fairness balance and confirming its generality across smart-city domains.