Fake News Detection Using Deep Learning-Based Natural Language Processing Models

Abstract

The rising popularity of social media websites has led to the increased usage of fake news, threatening the trust of the general public and the integrity of the society. As a result, the detection of fake news through deep learning approaches has now become an essential research topic. In this research study, the detection of fake news through deep learning approaches by utilizing natural language processing techniques is discussed. In the proposed approach, text processing and word embedding are used. The proposed approach uses deep learning techniques that give better syntactical as well as semantic insights of the news. The proposed approach uses different deep learning models such as Long Short-Term Memory (LSTM) Network and transformers. The experimental results show that the deep learning approach produces better accuracy, precision, recall, and F1-score values compared to existing machine learning approaches. The research study proves that deep learning approaches give accurate insights of the news and can be used as a scalable tool for fake news detection. Based on the experimental study of the research study, deep learning approaches can be implemented in the fake news detection system.

Introduction

The rapid growth of digital media and social networking platforms has increased access to information but has also accelerated the spread of fake news, which can negatively affect public opinion, democracy, and the economy. Traditional fake news detection methods—such as manual fact-checking and rule-based or feature-engineered machine learning models—are inefficient, labor-intensive, and unable to scale with the volume and complexity of online content. As a result, there is a growing need for automated and intelligent detection systems.

Recent advances in Natural Language Processing (NLP) and deep learning have significantly improved fake news detection. Unlike traditional approaches, deep learning models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and transformer-based architectures can automatically learn complex textual patterns and long-term dependencies without manual feature engineering. Among these, transformer models like BERT have demonstrated state-of-the-art performance due to their ability to capture bidirectional contextual information using self-attention mechanisms.

The literature shows a clear progression from traditional machine learning methods using handcrafted features (TF-IDF, n-grams) and classifiers (Naïve Bayes, SVM, Random Forest) to deep learning and hybrid approaches. Hybrid and ensemble models that combine textual, social, and behavioral information further enhance detection accuracy, especially on social media platforms. However, challenges remain in real-time detection, multilingual content handling, and identifying sophisticated misinformation.

The proposed methodology applies deep learning–based NLP techniques for fake news detection. It involves dataset preparation, text preprocessing, word embedding generation using models like Word2Vec or GloVe, and classification using LSTM and transformer-based models such as BERT. The system is trained using supervised learning and evaluated with standard metrics including accuracy, precision, recall, and F1 score. Overall, the study aims to demonstrate the superiority of deep learning and transformer-based models over traditional methods in accurately and efficiently detecting fake news.

Conclusion

In this paper, a deep learning–based methodology was proposed and analyzed to automatically detect fake news using techniques of natural language processing. Both LSTM and transformer-based models were utilized in the study to gain both sequential and contextual information from textual content, while pre processing and word embedding techniques were used to enhance feature representation. Experimental results showed that deep learning models, specifically transformer-based architectures, significantly outperform traditional machine learning approaches in terms of accuracy, precision, recall, and F1-score. The results have underlined the effectiveness of contextual embedding and deep neural architectures in the identification of deceptive patterns within news articles. The proposed methodology gives a scalable and robust framework for real-world fake news detection systems that can be integrated into social media platforms and content verification tools. Future work will investigate the inclusion of multimodal data, such as images and social network data, and the utilization of lightweight transformer models to perform real-time detection on large-scale datasets.

References

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Copyright

Copyright © 2026 Dr. Sangeetha Radhakrishnan. 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.