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 dete...
Alpana A. Borse, Gajanan K. Kharate, N. Wasatkar· Journal of Intelligent Decis...· 0 citations
From URL-sharing patterns in Telegram chats, a statistically validated domain co-sharing network is built and assortative mixing by reliability is found: low-reliability domains group together, as do reliable ones.
R. 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
A multimodal framework that produces a credibility classification and a propagation-level prediction within a single processing pipeline is evaluated that combines frozen RoBERTa features, engineered interaction statistics, a standard GCN, and Transformer self-attention over modality representations.
Long Yang, Wenxin Ma, Wei-Te 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. Babu, G. Sukanya· International Journal for Re...· 0 citations
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