Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Namruth Reddy
NVIDIA Corporation
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Enhancing fake news detection: a hybrid BERT-XGBoost model for improved performance and interpretability Nishant Vasantkumar Hegde; Suneesh Bare; Namruth Reddy; Rajat Gondkar Aravinda; Minal Moharir; Aamir Ibrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2385-2397

Abstract

The widespread spread of fake news poses a serious threat to the integrity of information. The dominant approach to detection involves end-to-end fine-tuning of large transformer models like bidirectional encoder representations from transformers (BERT), which, despite achieving high accuracy, often function as opaque “black boxes” with limited interpretability. This paper proposes and validates a hybrid, decoupled architecture that proves to be a more practical and powerful alternative. We first fine-tune a DistilBERT model on the full WELFake dataset of 71,537 articles after cleaning to create domain-specific embeddings. These high-dimensional vectors are then used as input features to train a robust extreme gradient boosting (XGBoost) classifier. The results demonstrate that the hybrid model achieves a state-of-the-art accuracy of 99.76%, slightly surpassing the already high performance of a standard end-to-end fine-tuned model. Crucially, this approach provides this top-tier performance while offering significant advantages in model interpretability through feature importance analysis. This work establishes that a decoupled architecture is not just a viable alternative but a superior practical strategy for combating misinformation, successfully balancing state-of-the-art accuracy with essential model transparency.
Co-Authors Aamir Ibrahim Minal Moharir Nishant Vasantkumar Hegde Rajat Gondkar Aravinda Suneesh Bare