Article: Automatic fake news detection using an optimised transformer with deer hunting optimisation algorithm Journal: International Journal of Information and Computer Security (IJICS) 2026 Vol.31 No.1 pp.24 - 48 Abstract: Fake news has become a significant issue, influencing public opinion and decision-making across various platforms. Traditional machine learning algorithms often struggle to accurately detect subtle contextual details within misleading information. Transformer-based models, with their self-attention mechanisms, are particularly well-suited for this task, as they capture deeper contextual relationships within the text. This study proposes an advanced fake news detection system utilising a transformer encoder model, focusing on textual analysis for precise classification. By leveraging the transformer's ability to identify complex patterns and contextual nuances, the system improves the detection of misleading or false information. To further enhance model performance, hyperparameter optimisation is applied using the deer hunting optimisation algorithm (DHOA). The system is evaluated on a benchmark dataset, demonstrating its effectiveness with high recall and precision metrics. The proposed approach addresses the escalating issue of misinformation by combining cutting-edge natural language processing with an innovative optimisation technique. For the proposed method, transformer encoders were used, achieving an accuracy of 98.71%, a recall of 98.7%, and an F-measure of 98.8% on dataset 1. on dataset 2, the model achieved an accuracy of 98.23%, a recall of 98.24%, and an F-measure of 98.36%. Inderscience Publishers - linking academia, business and industry through research

Title: Automatic fake news detection using an optimised transformer with deer hunting optimisation algorithm

Authors: Yamini Devi Jonnala; J. Sirisha Devi

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500043, Telangana, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500043, Telangana, India

Abstract: Fake news has become a significant issue, influencing public opinion and decision-making across various platforms. Traditional machine learning algorithms often struggle to accurately detect subtle contextual details within misleading information. Transformer-based models, with their self-attention mechanisms, are particularly well-suited for this task, as they capture deeper contextual relationships within the text. This study proposes an advanced fake news detection system utilising a transformer encoder model, focusing on textual analysis for precise classification. By leveraging the transformer's ability to identify complex patterns and contextual nuances, the system improves the detection of misleading or false information. To further enhance model performance, hyperparameter optimisation is applied using the deer hunting optimisation algorithm (DHOA). The system is evaluated on a benchmark dataset, demonstrating its effectiveness with high recall and precision metrics. The proposed approach addresses the escalating issue of misinformation by combining cutting-edge natural language processing with an innovative optimisation technique. For the proposed method, transformer encoders were used, achieving an accuracy of 98.71%, a recall of 98.7%, and an F-measure of 98.8% on dataset 1. on dataset 2, the model achieved an accuracy of 98.23%, a recall of 98.24%, and an F-measure of 98.36%.

Keywords: transformer encoders; deer hunting optimisation algorithm; DHOA; fake news; hyperparameter.

DOI: 10.1504/IJICS.2026.156919

International Journal of Information and Computer Security, 2026 Vol.31 No.1, pp.24 - 48

Received: 25 Sep 2025
Accepted: 23 Apr 2026
Published online: 12 Oct 2026 *

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Title: Automatic fake news detection using an optimised transformer with deer hunting optimisation algorithm

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