The rapid spread of climate change misinformation across digital platforms undermines scientific literacy, public trust, and evidence-based policy action. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) create new opportunities for automating the detection and correction of misleading climate-related narratives. This study presents a multi-stage system that employs state-of-the-art large language models such as Generative Pre-trained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA) version 3 (LLaMA-3), and RoBERTa-large (Robustly optimized BERT pretraining approach large) to identify, classify, and generate scientifically grounded corrections for climate misinformation. The system integrates several complementary techniques, including transformer-based text classification, semantic similarity scoring using Sentence-BERT, stance detection, and retrieval-augmented generation (RAG) for evidence-grounded debunking. Misinformation instances are detected through a fine-tuned RoBERTa–Multi-Genre Natural Language Inference (MNLI) classifier (RoBERTa-MNLI), grouped using BERTopic, and verified against curated climate-science knowledge sources using BM25 and dense retrieval via FAISS (Facebook AI Similarity Search). The debunking component employs RAG-enhanced GPT-4 to produce accurate and persuasive counter-messages aligned with authoritative scientific reports such as those from the Intergovernmental Panel on Climate Change (IPCC). A diverse dataset of climate misinformation categories covering denialism, cherry-picking of data, false causation narratives, and misleading comparisons is compiled for evaluation. Benchmarking experiments demonstrate that LLM-based models substantially outperform traditional machine-learning baselines such as Support Vector Machines, Logistic Regression, and Random Forests in precision, contextual understanding, and robustness to linguistic variation. Expert assessment further shows that generated debunking messages exhibit higher clarity, scientific accuracy, and persuasive effectiveness compared to conventional fact-checking text. These results highlight the potential of advanced LLM-driven pipelines to provide scalable, real-time mitigation of climate misinformation while offering guidelines for responsible deployment of AI-assisted debunking systems.

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.

Direct-to-consumer (DTC) genetic ancestry testing has grown rapidly, yet computational analysis of consumer reactions remains limited. This study presents a cross-platform computational analysis of consumer reactions to ancestry testing across 58,133 posts from Reddit, YouTube, and Google Play. We developed a six-category reaction taxonomy (acceptance, excitement, dispute, surprise, disappointment, identity crisis) and applied natural language processing methods including sentiment analysis, topic modeling, and predictive modeling. Results revealed that acceptance (9.5%) and excitement (9.4%) were most prevalent, followed by dispute (8.6%). Platform differences emerged: Reddit showed highest dispute rates (10.2%), while Google Play exhibited elevated excitement (29.6%). Dispute rates varied substantially by ancestry, with Turkish (23.5%), Greek (19.7%), and Scandinavian (18.5%) ancestries most frequently contested. Among posts containing both self-reported ethnicity and genetic results, concordance was 61.8%, quantifying the discrepancy between social and genetic definitions of ancestry. A logistic regression model predicting dispute expression achieved AUC = 0.79, identifying text length and negative sentiment as key predictors. These findings advance understanding of how consumers engage with genetic ancestry information online, with implications for DTC companies, genetic counselors, and researchers studying the social dimensions of consumer genomics.

We present FractalPINN-Flow, an unsupervised deep learning framework for dense optical flow estimation that learns directly from consecutive grayscale frames without requiring ground truth. The architecture centers on the Fractal Deformation Network (FDN) - a recursive encoder-decoder inspired by fractal geometry and self-similarity. Unlike traditional CNNs with sequential downsampling, FDN uses repeated encoder-decoder nesting with skip connections to capture both fine-grained details and long-range motion patterns. The training objective is based on a classical variational formulation using total variation (TV) regularization. Specifically, we minimize an energy functional that combines $L^1$ and $L^2$ data fidelity terms to enforce brightness constancy, along with a TV term that promotes spatial smoothness and coherent flow fields. Experiments on synthetic and benchmark datasets show that FractalPINN-Flow produces accurate, smooth, and edge-preserving optical flow fields. The model is especially effective for high-resolution data and scenarios with limited annotations.

We study Geraghty-type non-self mappings within the framework of best proximity point theory. By introducing auxiliary functions with subsequential convergence, we establish general conditions ensuring the existence and uniqueness of best proximity points. Our results extend and unify earlier work on proximal and Kannan-type contractions under a Geraghty setting, and provide counterexamples showing that the auxiliary assumptions are essential. As an illustration, we construct an explicit non-self alignment mapping on subsets of ${\mathbb{R}}^2$ for which all hypotheses can be verified and the unique best proximity point, as well as the convergence of the associated proximal iteration, can be computed in closed form.