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arXiv · 2206.07535

BaIT: Barometer for Information Trustworthiness

Oisín Nolan·Jeroen van Mourik·Callum Rhys Tilbury
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Abstract

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.

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Oisín Nolan, Jeroen van Mourik, Callum Rhys Tilbury. 2022-06-15. BaIT: Barometer for Information Trustworthiness. https://arxiv.org/abs/2206.07535

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