Detecting fake news with capsule neural networks

Fake news has increased dramatically 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 uptraining and updating in the training phase are used for medium length or long news statements. Moreover, we apply different levels of n-grams for feature extraction. Our proposed models 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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper explores the use of capsule neural networks for fake news detection, utilizing different embedding models and n-gram levels for feature extraction. Experimental results on the ISOT and LIAR datasets show promising performance compared to state-of-the-art methods.