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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

A gated recurrent unit-based model for fake news detection in online social networks

, , , *

* Corresponding author · click or hover a name for details

pp. 1841–1850Vol. 46Issue 6September 2025DOI: 10.47974/JIOS-2013XML
Received:
10 Dec 2024
Published Online:
01 Oct 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2013
Pages:
1841–1850

Abstract

Fake news and online rumors can mislead information, disrupt order, and destabilize society. This misleading information is propagated through online social media and other digital platforms, which results in loss of public confidence, stimulation of social unrest, and a threat to national security. In this work, we present a Gated Recurrent Unit-based fake news recognition approach to reduce the impact of misinformation. Since we want to build an effective model, we pre-process a fake-news-detection dataset having real and fake news by performing effective natural language processing (NLP) tasks such as news text cleaning, tokenization, word embedding, etc. The Gated Recurrent Unit (GRU) architecture is used based on its ability to execute sequential data, which allows the model to find patterns and the semantic relations within news content. The model proposed attained an accuracy of 99.55% and proved to be effective for detecting fake information. Our recommendation is to continue the fight against misinformation and other such cybercrimes. 

Keywords

Subject Classifications

68T5068T0768U35

References

[1] M. Choraś, K. Demestichas, A. Giełczyk, Á. Herrero, P. Ksieniewicz, K. Remoundou, D. Urda, and M. Woźniak, “Advanced machine learning techniques for fake news (online disinformation) detection: A systematic mapping study,” Appl. Soft Comput., vol. 101, pp. 107050 (2021).
[2] T. K. Mohammed, D. N. Vasundhara, S. H. Mehanoor, E. Sreedevi, P. R. Kumar, C. Manihass, and S. F. Baba, “A novel fusion approach for advancement in crime prediction and forecasting using hybridization of ARIMA and recurrent neural networks,” Journal of Information Systems Engineering and Management, vol. 10, no. 38, pp. 404–420 (2025).
[3] A. Bovet and H. A. Makse, “Influence of fake news in Twitter during the 2016 US presidential election,” Nat. Commun., vol. 10, no. 1, pp. 7 (2019).
[4] B. Shilpa, P. R. Kumar, and R. K. Jha, “LoRa DL: A deep learning model for enhancing the data transmission over LoRa using autoencoder,” The Journal of Supercomputing, vol. 79, pp. 17079–17097 (2023).
[5] A. A. Abbas and Z. M. Sallal, “Hybrid biometric authentication for banks security improvements,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 4-A, pp. 1037–1050 (2025).
[6] M. Hasani and M. Ghods, “Investigating fuzzy topological indices of linear and multi-cyclic Naphtalene,” Journal of Interdisciplinary Mathematics, vol. 28, no. 5, pp. 1779–1793 (2025).
[7] R. Ranjan, D. Pandey, A. K. Rai, D. Gupta, P. Singh, P. R. Kumar, and S. N. Mohanty, “A manifold-level hybrid deep learning approach for sentiment classification using an autoregressive model,” Applied Sciences, vol. 13, no. 5, pp. 3091 (2023).
[8] M. Pirani, P. Thakkar, P. Jivrani, M. H. Bohara, and D. Garg, “A comparative analysis of ARIMA, GRU, LSTM and BiLSTM on financial time series forecasting,” in Proc. IEEE Int. Conf. Distrib. Comput. Elect. Circuits Electron. (ICDCECE), pp. 1–6 (2022).
[9] G. B. Mohammad, Shitharth, and P. R. Kumar, “Integrated machine learning model for URL phishing detection,” International Journal of Grid and Distributed Computing, vol. 14, no. 1, pp. 513–529 (2021).
[10] D. Choudhury and T. Acharjee, “A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers,” Multimed. Tools Appl., vol. 82, no. 6, pp. 9029–9045 (2023).
[11] B. Shilpa, P. R. Kumar, and R. K. Jha, “Spreading factor optimization for interference mitigation in dense indoor LoRa networks,” in Proc. IEEE IAS Glob. Conf. Emerg. Technol. (GlobConET), pp. 1–5 (2023).
[12] D. Rohera, H. Shethna, K. Patel, U. Thakker, S. Tanwar, R. Gupta, and R. Sharma, “Taxonomy of fake news classification techniques: Survey and implementation aspects,” IEEE Access, vol. 10, pp. 30367–30394 (2022).
[13] S. Arvind, B. Unhelkar, S. S. Shankar, P. Chakrabarti, and S. V. Devika, “Advancing cyber threat detection through deep learning in management information systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 8, pp. 2409–2417 (2024).

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