A Deep Ensemble Framework for Fake News Detection and Classification

TL;DR

This paper presents a deep ensemble framework combining CNN and Bi-LSTM models for detecting and classifying fake news, achieving promising accuracy on a benchmark dataset.

Contribution

It introduces a novel ensemble approach that integrates CNN and Bi-LSTM representations for improved fake news detection and classification.

Findings

Achieved 44.87% accuracy on the benchmark dataset.

Outperformed existing state-of-the-art methods.

Demonstrated effectiveness of deep ensemble models in fake news detection.

Abstract

Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics. The rate of such information is increasing rapidly due to the availability of enormous web information sources including social media feeds, news blogs, online newspapers etc. In this paper, we develop various deep learning models for detecting fake news and classifying them into the pre-defined fine-grained categories. At first, we develop models based on Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations obtained from these two models are fed into a Multi-layer Perceptron Model (MLP) for the final classification. Our experiments on a benchmark dataset show promising results with an overall accuracy of 44.87\%, which outperforms the current state of the art.