Aug 2026· Applied and Computational Engineering· 0 citations
TL;DR
This work proposes a framework that combines BERT text embeddings with Graph Attention Network representations of propagation graphs that combines BERT text embeddings with Graph Attention Network (GAT) representations of propagation graphs.
Abstract
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.
Heterogeneous Graph Attention Network (HGAT) is proposed, 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.
Akash Garg, Sachin Pachauri· Journal of Artificial Intell...· 0 citations
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 and shows a detection accuracy rate of 96.00%, which beats multiple existing methodologies in fake news detection.
Alpana A. Borse, Gajanan K. Kharate, N. Wasatkar· Journal of Intelligent Decis...· 0 citations
Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag. We ask whether network structure can improve news reliability classification, taking a domain-level approach that shifts the focus from individual articles to source reliability. From URL-sharing patterns in Telegram chats, we build a statistically validated domain co-sharing network and find assortative mixing by reliability: low-reliability domains group together, as do reliable ones. Exploiting this structure, we compare Graph Neural Networks against network-unaware baselines using both content-aware features (multilingual text embeddings) and content-agnostic features (spreading dynamics). GNNs consistently outperform Multi-Layer Perceptrons on identical features, with GraphSAGE best in both settings (accuracy 0.63 with content, 0.53 without), a 13-14% relative gain over the network-unaware baseline. Network topology thus systematically improves domain reliability assessment, and remains effective even when content analysis is infeasible.
Raphaela Keßler, R. D. Ventzke, V. Priesemann et al.· 0 citations
HEF-XFND is proposed, a hybrid explainable feature-fusion framework that combines sparse lexical evidence, contextual transformer representations, source-level credibility indicators, and calibrated ensemble learning that addresses three recurring limitations in fake-news research.
Raju M, Subalakshmi Kannan, P. P.· International journal of res...· 0 citations
The rapid dissemination of misinformation through online social networks creates challenges for information reliability and network stability. We evaluate a multimodal framework that produces a credibility classification and a propagation-level prediction within a single processing pipeline. The framework combines frozen RoBERTa features, engineered interaction statistics, a standard GCN, and Transformer self-attention over modality representations. These are established components; the purpose of the framework is to integrate the available modalities and return both outputs from one model, rather than to introduce a new encoder or attention mechanism. On 12,701 MCFEND news items, the framework obtains 94.77% accuracy and 95.79% F1-score for credibility classification, together with an MAE of 0.34, RMSE of 0.44, and R2 of 0.96 for propagation-level prediction. The small variation across five matched seeds supports the stability of these means under the fixed protocol. The credibility F1 difference from modality-matched MLP-Fusion is not significant after Holm correction (adjusted p = 0.0810), and five pairs provide limited power for detecting small differences; the two implementations are therefore interpreted as having close performance. RandomForest also obtains lower propagation errors than the evaluated framework. The implemented retrospective fractional-observation protocol observes a fraction defined by final cascade size and uses snapshot engagement values with a fixed random split. In one diagnostic, replacing the fractional-observation rule with fixed K = 15 preserves classification F1 at a similar level but reduces the framework’s propagation R2 from 0.963 to 0.896. In a separate target-definition diagnostic conducted with the original observation setting, predicting residual future interactions yields an R2 of 0.870. The findings therefore characterize retrospective within-dataset prediction and do not establish leakage-free early forecasting. The two outputs are interpreted separately because the present experiments evaluated one shared dual-output configuration rather than comparing it with two independently optimized systems.
Long Yang, Wenxin Ma, Weite Li· Mathematics· 0 citations
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.
E. C. Babu, G. Sukanya· International Journal for Re...· 0 citations
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