# A Deep Ensemble Framework for Fake News Detection and Classification

arXiv: https://arxiv.org/abs/1811.04670 · HTML: https://mlsanity.com/paper/1811.04670/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Automated Evidence Collection for Fake News Detection](https://mlsanity.com/paper/2112.06507/) (2021)
- [UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu](https://mlsanity.com/paper/2207.05144/) (2022)

## Abstract (arXiv)

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.