Recognizing Fake News with Hybrid Optimization using Reinforced Transformer Models

Abstract

The widespread circulation of fake news on digital platforms presents serious challenges to the reliability of information and public trust. Existing detection methods often rely on static models and lack adaptability to multimodal inputs and uncertain predictions. This paper proposes a fake news recognition framework based on a reinforced transformer model combined with hybrid optimization. The system utilizes a fine-tuned BERT model to process both textual and image-derived content, enabling unified multimodal analysis. A reinforcement-driven mechanism is incorporated to adapt classification thresholds and attention behaviour based on prediction confidence. In addition, a hybrid optimization strategy that combines particle swarm optimization (PSO) and Dandelion Optimization (DO) is employed to tune key parameters such as learning rate and decision thresholds for improved performance. The model produces multi-label output classified as True, False, or Uncertain, where uncertain predictions correspond to low-confidence cases. To improve transparency, the system provides explainability through confidence scores, keyword extraction, attention insights, and reasoning-based outputs. The approach is evaluated on benchmark datasets including LIAR and DGM4. The proposed framework offers a practical and scalable solution for fake news detection by integrating adaptive learning, hybrid optimization, and interpretable results, making it suitable for real-world content verification applications.

Introduction

The text describes a fake news detection system designed to improve accuracy, adaptability, and explainability in identifying misleading online content. It highlights the growing problem of fake news on digital platforms and the limitations of traditional machine learning methods, which rely on manual feature extraction and struggle with contextual understanding. While transformer-based models like BERT have improved performance by capturing deeper language context, they still lack adaptability, multimodal support, and real-time decision flexibility.

To address these gaps, the proposed system introduces a multimodal fake news detection framework that processes both text and images using a fine-tuned BERT model. It incorporates a reinforcement-inspired confidence mechanism that categorizes outputs into True, False, and Uncertain, improving reliability by handling low-confidence predictions separately. The system also uses a hybrid optimization strategy combining Particle Swarm Optimization (PSO) and Dandelion Optimization (DO) to fine-tune model parameters and thresholds for better performance.

Additionally, the system emphasizes explainability by providing confidence scores, key keywords, and attention-based insights to help users understand predictions. A web-based interface enables real-time usage and accessibility.

The literature review shows the evolution from traditional machine learning (TF-IDF with classifiers like SVM and Naive Bayes) to deep learning models (CNN, RNN, LSTM), and then to transformer-based approaches like BERT, which achieve higher accuracy but still have limitations in handling multimodal data and post-2021 events. Recent multimodal and generative AI models improve image-text understanding, but deployment for real-world fake news detection remains limited.

Conclusion

This paper presents a structured implementation of the proposed project titled Recognizing Fake News with Hybrid Optimization using Reinforced Transformer Models. The framework combines a fine-tuned MT5 transformer, reinforcement-based learning using TRLX with ROUGE-1 reward, hybrid optimization through Particle Swarm Optimization and Dandelion Optimization, and multimodal processing using ImageBind. The developed system demonstrates that effective fake news detection can be achieved using a software-driven approach that integrates advanced NLP models with adaptive learning strategies. By combining reinforcement mechanisms with hybrid optimization, the framework improves model stability and decision-making without relying on static configurations. The proposed approach supports both text and image inputs, making it suitable for real-world scenarios where misinformation appears in multiple formats. The inclusion of an “Uncertain” category further enhances reliability by avoiding forced predictions in low-confidence situations. In addition, the system provides explainability through confidence scores, keyword extraction, and reasoning outputs, improving user trust and transparency. Experimental observations indicate that the system achieves consistent performance across different domains such as political, sports, and general news, with an overall accuracy of approximately 96.1%. The hybrid PSO–DO optimization contributed to stable predictions, while the reinforcement-based mechanism improved adaptive behaviour during model training. Another important aspect of this work is its practical deployment. The system has been implemented as a web-based application, enabling real-time interaction through a simple user interface. This highlights its usability for applications such as social media monitoring, content verification, and information filtering. Overall, the proposed framework demonstrates how transformer-based models, when combined with reinforcement learning and hybrid optimization, can provide a balanced solution for fake news detection. With future enhancements such as improved visual understanding, multilingual support, and deeper integration with online platforms, the system can evolve into a scalable solution for real-time misinformation analysis.

References

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Copyright

Copyright © 2026 Kanuri Ram Sai, Mr. Khadri Syed Faizz Ahmad, Abbana Kotireddy, Anagani Kasulu, Sayyad Mustaffa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.