# Fake News Identification on Twitter with Hybrid CNN and RNN Models

arXiv: https://arxiv.org/abs/1806.11316 · HTML: https://mlsanity.com/paper/1806.11316/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2018.

## Built on by (most cited first, 17 of 17)

- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model](https://mlsanity.com/paper/2309.04704/) (2023)
- [Methods of Informational Trends Analytics and Fake News Detection on Twitter](https://mlsanity.com/paper/2204.04891/) (2022)
- [MM-Claims: A Dataset for Multimodal Claim Detection in Social Media](https://mlsanity.com/paper/2205.01989/) (2022)
- [TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy](https://mlsanity.com/paper/2101.03529/) (2021)
- [BERT based classification system for detecting rumours on Twitter](https://mlsanity.com/paper/2109.02975/) (2021)
- [Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection](https://mlsanity.com/paper/2201.02048/) (2022)
- [MVAN: Multi-View Attention Networks for Fake News Detection on Social Media](https://mlsanity.com/paper/2506.01627/) (2025)
- [Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages](https://mlsanity.com/paper/2104.11761/) (2021)
- [A Study of Fake News Reading and Annotating in Social Media Context](https://mlsanity.com/paper/2109.12523/) (2021)
- [Evaluating BERT-based Pre-training Language Models for Detecting Misinformation](https://mlsanity.com/paper/2203.07731/) (2022)
- [Technological Approaches to Detecting Online Disinformation and Manipulation](https://mlsanity.com/paper/2108.11669/) (2021)
- [Evaluating Deception Detection Model Robustness To Linguistic Variation](https://mlsanity.com/paper/2104.11729/) (2021)
- [HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media](https://mlsanity.com/paper/2110.00425/) (2021)
- [A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks](https://mlsanity.com/paper/2112.00803/) (2021)
- [DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies](https://mlsanity.com/paper/2203.08144/) (2022)
- [Truth in Text: A Meta-Analysis of ML-Based Cyber Information Influence Detection Approaches](https://mlsanity.com/paper/2503.22686/) (2025)

## Abstract (arXiv)

The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain.