Detecting Fake News with Capsule Neural Networks
This addresses the problem of fake news proliferation on social media for users and platforms, but it is incremental as it adapts an existing method to a new task.
The paper tackled fake news detection by applying capsule neural networks with different embedding models and n-gram features, achieving performance improvements of 7.8% on ISOT and up to 3.1% on LIAR datasets over state-of-the-art methods.
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.