  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.
