BaIT: Barometer for Information Trustworthiness
This work addresses fake news detection, an important societal problem, but is incremental as it builds on existing methods with modest improvements.
The paper tackled the FNC-1 fake news classification task by using pre-trained encoder models from similar NLP tasks and exploring data augmentation to address class imbalance, achieving comparable overall performance with existing baselines while significantly increasing accuracy on an under-represented class.
This paper presents a new approach to the FNC-1 fake news classification task which involves employing pre-trained encoder models from similar NLP tasks, namely sentence similarity and natural language inference, and two neural network architectures using this approach are proposed. Methods in data augmentation are explored as a means of tackling class imbalance in the dataset, employing common pre-existing methods and proposing a method for sample generation in the under-represented class using a novel sentence negation algorithm. Comparable overall performance with existing baselines is achieved, while significantly increasing accuracy on an under-represented but nonetheless important class for FNC-1.