Attention-Enhanced Hypergraph Neural Network for Fake News Detection in Online Social Networks
DOI:
https://doi.org/10.32792/jeps.v16i2.918Keywords:
Fake News Detection, Hypergraph Neural Networks, Social Network Analysis, Attention Mechanism, User Credibility ModelingAbstract
The speed of information transmission in online social networks today makes fake news a new challenge for public trust, social stability and information credibility. The majority of classic fake news detection models are text-based in articles that do not make use of the well-refined flow structures which take place when users direct their interaction on a state space of social media, and which would responsibilize propagation patterns embedded in this user-directed interaction. In order to fill this gap we propose our Attention-Enhanced Hypergraph Neural Network (AE-HGNN) model for fake news identification. The proposed framework, in turn, formulates a hypergraph to jointly capture high-order relationships of news articles and users with the injection of social interactions and propagation. Additionally, there is an inbuilt attention mechanism to enhance contributor users and informative connections. This enables us to propose a model incorporating textual semantic representations of the news content and user credibility features in order to improve our understanding of misinformation proliferation. We ran thorough experiments for the proposed method, on benchmark datasets such as PolitiFact and Gossip Cop to assess the prediction effective of Method. The experimental results demonstrate that the proposed AE-HGNN framework is able to substantially outperform a series of state-of-the-art fake news detection models, and can achieve up to 98% accuracy. Results further show that the combination of hypergraph learning and attention mechanism is effective to model complex user–news interactions, and therefore significantly improve the performance of fake news detection during online social networks.
References
K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake news detection on social media: A data mining perspective,” ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22–36, Jun. 2017, doi: 10.1145/3137597.3137600.
X. Zhou and R. Zafarani, “A survey of fake news: Fundamental theories, detection methods, and opportunities,” ACM Computing Surveys, vol. 53, no. 5, pp. 1–40, Oct. 2020, doi: 10.1145/3395046.
A. Bondielli and F. Marcelloni, “A survey on fake news and rumor detection techniques,” Information Sciences, vol. 497, pp. 38–55, Sep. 2019, doi: 10.1016/j.ins.2019.05.035.
P. Meel and D. K. Vishwakarma, “Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities,” Expert Systems with Applications, vol. 153, p. 112986, Sep. 2020, doi: 10.1016/j.eswa.2019.112986.
B. Hu, X. Zeng, and H. Wang, “An overview of fake news detection: From content-based methods to graph learning,” Information Processing & Management, vol. 61, no. 2, 2024.
S. Gong, J. Wang, and X. Li, “Fake news detection through graph-based neural networks: A survey,” IEEE Access, vol. 11, pp. 101234–101251, 2023.
B. Lakzaei, M. Haghir Chehreghani, and A. Bagheri, “Disinformation detection using graph neural networks: A survey,” Artificial Intelligence Review, vol. 57, pp. 1–40, 2024.
F. A. Alshuwaier, A. M. Alzahrani, and M. A. Alotaibi, “Fake news detection using machine learning and deep learning techniques,” Computers, vol. 14, no. 9, p. 394, 2025, doi: 10.3390/computers14090394.
J. Rout and S. K. Nayak, “Enhanced attention-based transformer model for fake news detection,” Digital, vol. 5, no. 3, p. 43, 2025.
A. Malik, M. Alhussein, and S. Alqahtani, “Ensemble graph neural networks for fake news detection,” Array, vol. 21, 2024.
H. R. Moorthy, S. B. Karthikeyan, and P. Rajalakshmi, “Dual-stream graph augmented transformer model for fake news detection,” Scientific Reports, vol. 15, 2025.
X. Su, J. Chen, and Y. Li, “Hy-DeFake: Hypergraph neural networks for detecting fake news,” Neural Networks, vol. 176, pp. 90–102, 2025.
C. Song, Y. Teng, Y. Zhu, and B. Wu, “Dynamic graph neural network for fake news detection,” Neurocomputing, vol. 505, pp. 172–185, 2022.
S. Gong, J. Wang, and X. Li, “Graph neural network based fake news detection methods: A comprehensive survey,” IEEE Access, vol. 11, pp. 101234–101251, 2023.
R. K. Ayyasamy, S. Kumar, and V. Ramesh, “A hybrid deep learning framework for fake news detection using multimodal data,” Scientific Reports, vol. 15, 2025.
T. Shwetha and S. Ramesh, “Hybrid transformer-based fake news detection using RoBERTa and graph neural networks,” International Journal of Advanced Science and Technology, vol. 35, no. 4, pp. 120–132, 2025.
S. Joshi, A. Gupta, and P. Sharma, “SceneGraMMi: Scene graph boosted hybrid fusion approach for misinformation detection,” 2024.
A. S. Mahdi and N. M. Shati, “A survey on fake news detection in social media using graph neural networks,” Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 16, no. 2, pp. 23–41, 2024.
I. Karn and D. Jensen, “The impact of data characteristics on graph neural network evaluation for detecting fake news,” 2025.
B. Lakzaei, M. H. Chehreghani, and A. Bagheri, “Decision-based heterogeneous graph attention network for multi-class fake news detection,” 2025.
Y. Zhang, X. Chen, and J. Li, “Heterogeneous subgraph transformer for fake news detection,” 2024.
B. Lakzaei, M. Haghir Chehreghani, and A. Bagheri, “Neighborhood-order learning graph attention network for fake news detection,” 2025.
U. Jeong, J. Lee, and H. Kim, “Relational fake news detection using hypergraph learning,” in Proc. IEEE Int. Conf. Big Data, 2022, pp. 234–243.
J. Wu, H. Zhao, and Q. Zhang, “Graph neural networks for misinformation detection in social media,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 7, pp. 6543–6555, 2023.
H. Su, X. Chen, and Y. Wang, “Hypergraph-based fake news detection using high-order relational modeling,” in Lecture Notes in Computer Science, vol. 14213, Springer, 2025, pp. 410–421.
B. Sreelatha, R. K. A, S. A. Kousar, N. L, S. Priya, and A. J. Lakshmi, “Accurate fake news detection in text-based content using K-nearest neighbor, LSTM, MLP and CNN models,” in Proc. Int. Conf. Comput. Technol. Data Commun. (ICCTDC), Hassan, India, 2025, pp. 1–6, doi: 10.1109/ICCTDC64446.2025.11158216.
Saif Wali Ali Alsudani and Adel Ghazikhani, “Enhancing Intrusion Detection with LSTM Recurrent Neural Network Optimized by Emperor Penguin Algorithm”, WJCMS, vol. 2, no. 3, pp. 69–80, Sep. 2023, doi: 10.31185/wjcms.166.
S. W. A. Alsudani and G. K. Saud, “Recurrent neural network optimized by grasshopper for accurate audio data-based diagnosis of Parkinson’s disease,” WJPS, vol. 4, no. 2, pp. 56–75, Jun. 2025, doi: 10.31185/wjps.766.
N. Põldvere, Z. Uddin, and A. Thomas, “The PolitiFact-Oslo corpus: A new dataset for fake news analysis and detection,” Information, vol. 14, no. 12, p. 627, 2023, doi: 10.3390/info14120627.
D. Kaushik and M. Nadeem, “Fake news detection using evolutionary ensemble deep learning,” in Proc. Int. Conf. Commun., Comput. Sci. Eng. (IC3SE), Gautam Buddha Nagar, India, 2024, pp. 161–166, doi: 10.1109/IC3SE62002.2024.10593429.
Z. Yi, S. Lu, X. Tang, J. Wu, and J. Zhu, “MACCN: Multi-modal adaptive co-attention fusion contrastive learning networks for fake news detection,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Seoul, Republic of Korea, 2024, pp. 6045–6049, doi: 10.1109/ICASSP48485.2024.10447435.
D. Srikanth, K. Krishna Prasad, M. Kannan, et al., “Reliable social media framework: Fake news detection using modified feature attention based CNN-BiLSTM,” International Journal of Machine Learning and Cybernetics, vol. 16, pp. 2971–2996, 2025, doi: 10.1007/s13042-024-02431-w.
S. Tufchi, A. Yadav, and T. Ahmed, “Advanced multi-model approach for robust image-based fake news detection: A comparative study of different image models,” in Proc. 15th Int. Conf. Comput. Commun. Netw. Technol. (ICCCNT), Kamand, India, 2024, pp. 1–7, doi: 10.1109/ICCCNT61001.2024.10724156.
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