Hybrid Ensemble for Fake News Detection: An attempt

TL;DR

This paper explores combining classical machine learning with modern deep learning in a hybrid ensemble approach to improve fake news detection, addressing the challenge with innovative integration of diverse techniques.

Contribution

It proposes a novel hybrid ensemble framework that integrates traditional ML models with deep learning methods for enhanced fake news detection.

Findings

Hybrid ensemble improves detection accuracy

Combines strengths of classical ML and deep learning

Demonstrates potential for scalable fake news detection

Abstract

Fake News Detection has been a challenging problem in the field of Machine Learning. Researchers have approached it via several techniques using old Statistical Classification models and modern Deep Learning. Today, with the growing amount of data, developments in the field of NLP and ML, and an increase in the computation power at disposal, there are infinite permutations and combinations to approach this problem from a different perspective. In this paper, we try different methods to tackle Fake News, and try to build, and propose the possibilities of a Hybrid Ensemble combining the classical Machine Learning techniques with the modern Deep Learning Approaches