Balancing the truth: social media misinformation detection via GraphSAGE and feature diversity

A Heterogeneous Graph Attention Network with Pre-trained Language Models for Multi-Modal Fake News Detection

The spreading of fake news via online social media has emerged as one of the major issues in the modern information systems. The existing fake news detection techniques mainly rely on text-based classification methods and are ineffective in incorporating multimodal features, which can be used for fake news detection and classification. This study proposes HGAT-BERT, where a pretrained BERT-Large encoder is coupled with a Heterogeneous Graph Attention Network (HGAT) to learn joint representations for textual, social network and external knowledge graph features. Cross-modal attention and gated residual connections facilitate the integration of these heterogeneous feature streams into a unified representation. On the FakeNewsNet dataset split on PolitiFact and GossipCop data, the research reports an accuracy of 93.7% and an F1 score of 93.2%, an improvement of around 3.5% over the existing methods. The ablation analysis shows that all components represent meaningful contributions to the overall model performance, with cross-modal attention being responsible for the largest marginal contribution.

Integrating crossbred node feature encoding with tree and analogous graph attention networks for comprehensive twitter analysis

4HAN: An Enhanced Neural Network for Fake News Detection using Hypergraph

Due to the fast proliferation of online news media and social networks, there is a significant increase in the dissemination of misleading data and fake news on digital platforms. Fake news detection is difficult due to the incapacity of deep learning models or conventional machine learning in comprehending semantic and higher-level interactions between news text, news author, publisher, and additional metadata. This results in decreased detection efficiency and enables the dissemination of misinformation that impacts societal behavior and public opinion. To address this problem, a Four-Level Hierarchical Attention Network (4HAN) that incorporates word-, sentence-, and headline-level attention, along with Hypergraph Convolution and Hypergraph Attention, is proposed using the LIAR dataset. By combining semantic feature extraction and relational dependency modeling, the 4HAN framework achieves better results. Results showed a detection accuracy rate of 96.00%, which beats multiple existing methodologies in fake news detection. These findings demonstrate that combining hierarchical attention with hypergraph learning provides more effective semantic and relational representation, leading to improved fake news detection performance and greater robustness in misinformation analysis.

Multimodal Fusion Model for Fake News Detection Combining BERT and Graph Attention Networks

Fake-news detection needs both semantic and social evidence: text-only systems miss coordinated diffusion, while graph-only systems may confuse virality with falsehood. We propose a framework that combines BERT text embeddings with Graph Attention Network (GAT) representations of propagation graphs. Projected features are aligned by a cross-modal consistency loss and combined through an adaptive attention gate; when graphs are unavailable, the structural branch is masked. FakeNewsNet and Twitter15/16 evaluate full fusion, while LIAR tests text-only operation. On FakeNewsNet, the model reaches 93.4% accuracy, 92.8% Macro-F1, and 0.97 AUC. Ablations support both fusion components, and token- and node-level attribution assists human review.

OTPCL: Optimal Transport Driven Pseudo-Labeling with Contrastive Learning for Social Bot Detection

Social bot detection is vital for protecting online platforms from misinformation and manipulation. In recent research, graph neural networks (GNNs) have emerged as a powerful approach, since they leverage relational patterns and social interactions to identify coordinated bot behaviors. However, two key challenges arise from the nature of real-world social networks: First, bots often actively interact with human users through follows, replies, and mentions, creating numerous ''heterophilous'' edges, i.e., connections between different classes. These cross-class ties disrupt the homophily assumption underlying many GNNs, causing messagepassing to propagate and amplify errors. Second, due to the high cost and time required for manual annotation, social media platforms typically contain a large proportion of unlabeled data, with only a small fraction labeled for bot detection. Unlabeled data are often underutilized, making supervision sparse. To address this, we propose OTPCL (Optimal Transport Driven Pseudo-Labeling with Contrastive Learning), a plug-in framework for GNN-based social bot detection. OTPCL first employs contrastive learning to obtain well-separated node representations. It then formulates pseudolabel assignment as an optimal transport problem, which simultaneously generates pseudo-labels and quantifies their reliability via transport scores. These scores guide two key mechanisms: selective removal of unreliable heterophilous edges to purify the graph structure, and reducing the influence of pseudo-labels with transport scores below the dynamic threshold. Extensive experiments on three widely used benchmark datasets demonstrate that OTPCL consistently improves the detection performance across six different GNN backbones, showing strong robustness and generalization in both low-labeled and fully-labeled scenarios.

A Multi-Model Learning Framework for Fake News Detection on Social Media

The rapid proliferation of user-generated content on social media platforms has transformed the way information is created, shared, and consumed. While this democratization of information has clear benefits, it has also enabled the large-scale dissemination of fake news, which poses serious threats to public health, political stability, financial markets, and social cohesion. Automatic fake news detection has therefore become an active and urgent research problem in computer science. This paper proposes a multi-model learning framework that combines the complementary strengths of classical machine learning classifiers, deep sequential neural networks, and transformer-based contextual language models to detect fake news on social media. The framework integrates lexical, semantic, and contextual features through a weighted soft-voting ensemble that fuses predictions from a Support Vector Machine, a Bidirectional Long Short-Term Memory network, and a fine-tuned BERT encoder. Experiments were conducted on three widely used public benchmark datasets, namely LIAR, FakeNewsNet, and ISOT, comprising more than one hundred thousand labeled news statements and articles. The proposed ensemble achieved an accuracy of 96.4% and an F1-score of 96.1% on the ISOT dataset, outperforming each individual constituent model and several competitive baselines reported in the literature. Ablation studies confirm that the transformer component contributes the largest performance gain, while the ensemble fusion improves robustness and reduces variance across datasets. The results demonstrate that combining heterogeneous learning paradigms yields a more accurate and generalizable fake news detector than any single model in isolation. The paper also discusses computational cost, interpretability, and limitations, and outlines directions for multimodal and cross-lingual extensions.