Jul 2026· Journal of Artificial Intelligence and Capsule Networks· Vol 8, pp. 243-255· 0 citations· 17 references
TL;DR
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.
Abstract
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.
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
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.
Jiale Wang· Applied and Computational En...· 0 citations
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 is proposed.
Priya Verma· International Journal of Res...· 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
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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