Misinformation on social media can be a severe threat to social trust, safety, and health of the population, especially in times of an epidemic like the Monkeypox outbreak. This study specifically focuses on a hybrid RoBERTa–GRU architecture designed to capture both contextual semantics and temporal dependencies in social media discourse. This research presents how the combination of text mining and social network analysis enables Artificial Intelligence (AI) to support misinformation detection. The proposal of a hybrid architecture that integrates RoBERTa and GRU-based embeddings in a contextual fashion and GRU-based modelling of sequential patterns helps identify and substantiate misinformation in social media posts. Based on a curated X (formerly Twitter) dataset consisting of Monkeypox posts (5787 posts), the model provided state-of-the-art results, with ROC-AUC 0.9979 and Cohen’s kappa 0.9887; standalone baselines were also surpassed. Results reveal that the proposed transformer–RNN hybrid effectively captures both semantic depth and temporal relationships in misinformation detection tasks. In addition to performance, the paper addresses the limitations of dataset bias, multilingual constraint issues, and scalability as related to cross-linguistic applicability, multimodal study, and performance in real-time and resource-limited systems. The study adds value in this emerging body of knowledge on AI-driven social media analytics by offering practical guidance for mitigating health-related misinformation online.
Arafat Rohan, Md Asraful Islam, Areyfin Mohammed Yoshi et al.· Scientific Reports· 0 citations
Modern digital infrastructure generates heterogeneous, multimodal software artifacts encompassing structured code metrics, unstructured textual data such as commit logs and inline comments, and dynamic runtime signals whose complexity renders traditional defect detection approaches increasingly inadequate. This paper presents a cloud-native, transformer-based multimodal intelligence framework that integrates Large Language Model (LLM) semantic encoding with deep neural learning to enable automated defect prediction and proactive maintenance of large-scale digital infrastructure. The proposed system employs a sentence-transformer encoder (all-MiniLM-L6-v2) to process multimodal software artifact data—including serialized structured metrics and available textual fields—into dense 384-dimensional semantic embeddings. These embeddings are subsequently refined through a hierarchical multi-layer perceptron (MLP) deployed on a scalable cloud architecture for real-time inference. Evaluated on a real-world dataset of approximately 60,000 software modules, the framework achieves 99.72% accuracy, 100% precision, and an ROC-AUC of 0.9998, substantially outperforming baseline models including Random Forest, XGBoost, LSTM, and standalone MLP architectures. To address potential concerns regarding result validity, we conducted repeated experiments with five different random seeds (42, 0, 1, 7, 123) and ten-fold stratified cross-validation, confirming that performance metrics are stable across runs (accuracy: 0.9972 ± 0.0003). Data leakage was ruled out through strict temporal split ordering and pre-split SMOTE application exclusively on training folds. Confusion matrix and threshold-based analyses confirm strong classification performance with minimal false positives. SHAP-based explainability analysis further enhances the trustworthiness of the system by identifying the most influential multimodal predictors—past defect history, static analysis signals, and cyclomatic complexity—thereby contributing to transparent and accountable AI-driven infrastructure management. The presented framework advances the state of the art in LLM-driven multimodal systems by demonstrating how transformer intelligence, when applied to heterogeneous software artifact data streams, can enable reproducible, cloud-scalable, and interpretable maintenance pipelines for complex digital environments.
Mst Masuma Akter Semi, Md Masud Karim Rabbi, K. Ahmed et al.· Multimedia· 0 citations
