A Robust Hybrid Ensemble Model with Deep Feature Engineering Techniques for Fake News Detection in Social Media
Received: 3 September 2025 | Revised: 3 October 2025, 14 October 2025, 21 October 2025, and 23 October 2025 | Accepted: 25 October 2025 | Online: 8 December 2025
Corresponding author: K. S. Kavitha
Abstract
This study introduces a Hybrid Ensemble Model that combines several powerful methods to enhance the accuracy and reliability of fake news detection. Term Frequency-Inverse Document Frequency (TF-IDF) was utilized to highlight important words, and a one-dimensional Convolutional Autoencoder (1-D CAE) was used to capture deeper patterns in text. Subsequently, Pearson correlation was applied to filter the irrelevant information, keeping only the most useful features. These refined features were then passed through a combination of three classifiers: Extreme Gradient Boosting (XGBoost), Deep Forest (DF), and an Adaptive Layer-Based BERT (ALB) model. Final predictions were made through a weighted voting mechanism. The evaluation metrics were estimated on the FA-KES and LIAR datasets and a comparison with previous models, including Transformer+T5, XLNet+GPT-3, and BERT+GPT-2, was conducted. The results revealed 98.75% accuracy, 97.7% precision, 100% recall, and an F1-score of 98.83% on the FA-KES dataset, as well as 98.63% accuracy, 98.32% precision, 98.90% recall, and an F1-score of 98.61% on the LIAR dataset. These results demonstrate that the proposed approach can effectively handle the challenges of detecting fake news in social media.
Keywords:
fake news, term frequency-inverse document frequency, one-dimensional convolutional autoencoder, extreme gradient boosting, deep forest, adaptive layer-based BERT, Pearson correlationDownloads
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