# Detecting Fake News with Capsule Neural Networks

arXiv: https://arxiv.org/abs/2002.01030 · HTML: https://mlsanity.com/paper/2002.01030/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Multimodal Fake News Detection](https://mlsanity.com/paper/2112.04831/) (2021)
- [Fake News Detection: Experiments and Approaches beyond Linguistic Features](https://mlsanity.com/paper/2109.12914/) (2021)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)
- [A Proposed Bi-LSTM Method to Fake News Detection](https://mlsanity.com/paper/2206.13982/) (2022)
- [Fake News Detection Tools and Methods -- A Review](https://mlsanity.com/paper/2112.11185/) (2021)
- [X-CapsNet For Fake News Detection](https://mlsanity.com/paper/2307.12332/) (2023)
- [A Decision-Based Heterogenous Graph Attention Network for Multi-Class Fake News Detection](https://mlsanity.com/paper/2501.03290/) (2025)
- [A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model](https://mlsanity.com/paper/2202.01907/) (2022)

## Abstract (arXiv)

Fake news is dramatically increased in social media in recent years. This has prompted the need for effective fake news detection algorithms. Capsule neural networks have been successful in computer vision and are receiving attention for use in Natural Language Processing (NLP). This paper aims to use capsule neural networks in the fake news detection task. We use different embedding models for news items of different lengths. Static word embedding is used for short news items, whereas non-static word embeddings that allow incremental up-training and updating in the training phase are used for medium length or large news statements. Moreover, we apply different levels of n-grams for feature extraction. Our proposed architectures are evaluated on two recent well-known datasets in the field, namely ISOT and LIAR. The results show encouraging performance, outperforming the state-of-the-art methods by 7.8% on ISOT and 3.1% on the validation set, and 1% on the test set of the LIAR dataset.