Title: Deep learning to detect social media fake news using sequence generative adversarial networks

Authors: Saravanan Venkataraman; S. Albert Antony Raj; S. Belina V.J. Sara; S. Silvia Priscila

Addresses: College of Technology and Business, Riyadh ELM University, Riyadh, 12734, Kingdom of Saudi Arabia ' Faculty of Science and Humanities, College of Sciences, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, 603203, Tamil Nadu, India ' Department of Computer Applications, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, 603203, Tamil Nadu, India ' Department of Computer Science, Bharath Institute of Higher Education and Research (BIHER), Tamil Nadu, India

Abstract: Even if major efforts were made to verify the facts, the rising amount of fake news on social media, which has had a major influence on righteousness, confidence in others, and the community, persisted. This study offers SeqGAN to improve social media false news detection. By solving social media content's unique problems, SeqGAN proves its sequence creation capabilities. The version uses SeqGAN's generative energy to generate sensible text sequences and a discriminator network to identify bogus information narratives. Due to more accurate synthetic data, this unfavourable training method challenges the discriminator. The model's ability to detect misinformation is tested here. It also evaluates SeqGAN on large datasets and uses multiple false news detection approaches to provide an overview. These findings illustrate the rise of bogus news, which could propagate on social media using SeqGAN. Experimental results reveal that the SeqGAN model outperforms standard false news detection approaches. The model is more sensitive to misinformation campaign linguistic and temporal quirks. The SeqGAN-based method works well in social media and online chat environments. Python's model classifies instances with 98.5% accuracy and great proficiency, indicating its robustness in identifying truth.

Keywords: sequence generative adversarial nets; SeqGAN; false news detection; social media; generative adversarial networks; misinformation campaigns.

DOI: 10.1504/IJESDF.2026.155004

International Journal of Electronic Security and Digital Forensics, 2026 Vol.18 No.4, pp.434 - 461

Received: 10 May 2024
Accepted: 19 Aug 2024
Published online: 22 Jul 2026 *

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