mlsanityData to 13 Oct 2026

BaIT: Barometer for Information Trustworthiness

arXiv 2206.07535 ↗First posted 15 Jun 2022cs.LG cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 15 Jun 2022.

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13 of 33 references.

References · 13

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  11. 2018Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake Newscited by 11
  12. 2019Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Taskcited by 7
1 more
  1. 2017On the Benefit of Combining Neural, Statistical and External Features for Fake News Identificationcited by 3

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