International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 12 (December 2025), Pages: 142-157

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 Original Research Paper

A recurrent neural network model for detecting fake news on social media

 Author(s): 

 Keshav Kaushik 1, Gunjan Chhabra 2, Salil Bharany 3, Sghaier Guizani 4, Ateeq Ur Rehman 5, *, Habib Hamam 6, 7, 8, 9

 Affiliation(s):

  1Department of Computer Science & Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, Uttar Pradesh, India
  2Department of CSE, Graphic Era Hill University, Dehradun, India
  3Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
  4College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
  5School of Computing, Gachon University, Seongnam-si 13120, South Korea
  6Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada
  7School of Electrical Engineering, University of Johannesburg, Johannesburg, South Africa
  8International Institute of Technology and Management (IITG), Av. Grandes Ecoles, Libreville BP 1989, Gabon
  9Bridges for Academic Excellence-Spectrum, Tunis, Tunisia

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-5203-0621

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.12.014

 Abstract

The growing popularity of social media platforms for sharing news and videos has made it easier for users to access and share information instantly. However, verifying the credibility of such content remains a significant challenge. These platforms enable the rapid spread of fake news, which often leads to the distribution of inaccurate information. Since social media content is largely unrestricted, users frequently share news without verifying its source or accuracy, causing fake news to spread quickly and sometimes go viral. This can have harmful effects on society. Therefore, it is essential to ensure that the news shared on social media is accurate to prevent users from being misinformed, which is crucial for positive social development. This study proposes a recurrent neural network model using artificial intelligence and machine learning to detect and verify fake news on social media. The framework includes steps such as defining the problem, using datasets labeled as "fake" and "true," and applying natural language processing techniques. The authors conducted data cleaning, feature engineering, and visualization of real and fake news before converting text into tokens. The proposed model achieved a high accuracy of 99.96% with a minimal loss of 0.0083 after processing over 14 million tokens using 128 layers.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Fake news detection, Social media credibility, Recurrent neural network, Machine learning model, Natural language processing

 Article history

 Received 17 August 2024, Received in revised form 5 January 2025, Accepted 24 November 2025

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Kaushik K, Chhabra G, Bharany S, Guizani S, Rehman AU, and Hamam H (2025). A recurrent neural network model for detecting fake news on social media. International Journal of Advanced and Applied Sciences, 12(12): 142-157

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 Figures

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 Tables

  Table 1 

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