Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 156-171· 0 citations
TL;DR
A Hybrid Vision-Language Stacked Ensemble Model that combines deep semantic features from EfficientNet-B0 and BERT with traditional ensemble learning techniques to handle multimodal misinformation detection is presented.
Abstract
Multimodal misinformation has been identified as a key challenge in social media platforms, where the misleading information is presented using both textual and visual modalities. The current state-of-the-art methods for detecting misinformation are primarily unimodal or based on monolithic deep learning models that lack the ability to generalize across different patterns of misinformation. This paper presents a Hybrid Vision-Language Stacked Ensemble Model that combines deep semantic features from EfficientNet-B0 and BERT with traditional ensemble learning techniques to handle multimodal misinformation detection. In particular, high-level image features were extracted using EfficientNet-B0 and text embeddings using a pre-trained BERT model. The learned multimodal feature representations were then used by a stacked ensemble classifier that leverages the complementary capabilities of Random Forest, Gradient Boosting, and a Logistic Regression meta-learner. Experiments conducted on the MMFakeBench dataset for binary and multiclass misinformation classification tasks have shown that the proposed framework achieves better performance compared to individual base learners and conventional fusion approaches. Further detailed evaluation had also validated the efficacy of modality-wise feature fusion and the robustness of the ensemble learning approach against class imbalance and noisy data. The proposed framework provides a scalable, interpretable, and robust solution for real-world multimodal misinformation detection on social media platforms.
H-FuseNet is proposed, a hybrid multi-representation fusion framework that combines pretrained transformer representations with deception-oriented handcrafted linguistic, stylistic, and semantic features that can improve the reliability of automated fake news detection systems.
A. Mahmood, Marek Sikora, M. Ather et al.· Machine Learning and Knowled...· 0 citations
With the rapid growth of social media, online news has become increasingly multimodal, combining textual and visual information, posing new challenges for fake news detection. Existing methods often suffer from redundant features, distribution differences across modalities, and insufficient modeling of semantic interactions. To address these issues, this paper proposes an Adaptive Binary Osprey Optimization Algorithm and Cross-modal Disentangled Fusion model (ABOOA-CDF). First, an Adaptive Binary Osprey Optimization Algorithm (ABOOA) is developed for multimodal feature selection by integrating chaotic initialization, adaptive search, and binary mapping strategies to identify informative feature subsets. Then, a Cross-modal Relation Disentanglement Module (CRDM) is introduced to decompose multimodal representations into shared, discrepant, and complementary components, thereby enhancing semantic relationship modeling. Furthermore, an Adaptive Semantic Fusion Module (ASFM) dynamically learns fusion weights to generate discriminative multimodal representations. Experimental results demonstrate that ABOOA-CDF effectively improves detection performance. Compared with MFO and OOA, the proposed method achieves Accuracy improvements of 1.02 and 2.66 percentage points, respectively, verifying its effectiveness in feature optimization, cross-modal relation modeling, and semantic fusion.
A hybrid transformer-based ensemble model for automated fake news identification using the FakeNewsNet dataset is proposed and Experimental results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent models.
M. E. Babu, G. Sukanya· International Journal for Re...· 0 citations
Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty levels. We propose ARMDIL, an Adaptive Router for Multi-Domain Image Classification with LLMs. ARMDIL is an ensemble that uses a multimodal large language model (MLLM) agent to dynamically route each image to the most suitable vision backbone. Our diverse ensemble employs convolutional neural networks (ResNets), self-supervised representation learners (SSL), and vision language models (VLMs), each trained on a unified label space constructed from multiple image datasets with differing distributions and characteristics. Empirical evaluations illuminate the distinct capabilities and vulnerabilities of each architecture across disparate visual domains. Crucially, we show that ARMDIL effectively navigates these tradeoffs, performing competitively with specialized training-based routers. Furthermore, it drastically improves adaptability by allowing new information to be integrated via simple prompt modifications, while enhancing interpretability through natural language reasoning traces. These advances in cross-dataset image classification pave the way for more reliable general-purpose vision systems such as AI assistants and autonomous robots.
Daniel A. Perkins, John Squires, Janou Milligan et al.· 0 citations
Results indicate that the proposed architecture successfully combines the process of refining the semantic features of text data while providing an explainable artificial intelligence solution for real-world applications of fake news detection.
Sudha Patel, Shilpa Serasiya, Sachi Bhavsar et al.· International journal of com...· 0 citations
The rapid dissemination of rumors on social media at their early stages poses significant threats to public safety and social stability. While response-based methods usually depend on user comments and reposts and therefore suffer from inherent latency, existing content-based methods still struggle to extract discriminative evidence from noisy short texts and subtle visual inconsistencies under zero-response conditions. To address this issue, we propose SMD-Net, a multimodal framework for early zero-response rumor detection. In the textual branch, a selective state-space encoder is used to model fragmented and noisy posts. In the visual branch, an enhanced TransXNet backbone is designed to improve the representation of fine-grained suspicious patterns and cross-layer feature interactions. An adaptive gated fusion module is further introduced to integrate textual and visual features for final prediction. Experiments on the Weibo and PHEME datasets show that SMD-Net outperforms the compared content-based baselines, achieving 92.60% accuracy on Weibo and 90.27% accuracy on PHEME under the strict zero-response setting. These results suggest that the proposed framework provides an effective solution for early multimodal rumor detection when propagation-based evidence is unavailable.
