Fake News Detection using Machine Learning

TLDR

An advanced multimodel framework that integrates deep learning architectures — specifically Bidirectional Long Short-Term Memory and a fine-tuned BERT-based ensemble and a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model — alongside classical machine learning approaches, enabling interpretable prediction outputs is proposed.

Abstract

The exponential growth of social media has created an unprecedented environment for the rapid dissemination of

misinformation. Existing fake news detection systems predominantly rely on either textual features or propagation patterns in

isolation, limiting their effectiveness against sophisticated misinformation campaigns. This paper proposes an advanced multimodel framework that integrates deep learning architectures — specifically Bidirectional Long Short-Term Memory (BiLSTM)

and a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model — alongside classical machine

learning approaches including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, and XGBoost.

The models are further combined through an ensemble voting strategy to maximize classification accuracy. Experiments

conducted on the LIAR and FakeNewsNet benchmark datasets demonstrate that the proposed BERT-based ensemble achieves a

peak accuracy of 96.4%, outperforming individual classical models by a significant margin. Additionally, model explainability is

incorporated through SHAP (SHapley Additive explanations) values, enabling interpretable prediction outputs. The results

validate the superiority of combining deep contextual embeddings with classical feature engineering for robust fake news

detection.