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Transforming Fake News: Robust Generalisable News Classification Using Transformers

Blackledge, Ciara; Atapour-Abarghouei, Amir

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Ciara Blackledge


As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which transformers have achieved state-of-the-art results. Using the publicly available ISOT and Combined Corpus datasets, this study explores transformers’ abilities to identify fake news, with particular attention given to investigating generalisation to unseen datasets with varying styles, topics and class distributions. Moreover, we explore the idea that opinion-based news articles cannot be classified as real or fake due to their subjective nature and often sensationalised language, and propose a novel two-step classification pipeline to remove such articles from both model training and the final deployed inference system. Experiments over the ISOT and Combined Corpus datasets show that transformers achieve an increase in F1 scores of up to 4.9% for out of distribution generalisation compared to baseline approaches, with a further increase of 10.1% following the implementation of our two-step classification pipeline. To the best of our knowledge, this study is the first to investigate generalisation of transformers in this context.


Blackledge, C., & Atapour-Abarghouei, A. (2021). Transforming Fake News: Robust Generalisable News Classification Using Transformers. .

Conference Name 2021 IEEE International Conference on Big Data (IEEE BigData 2021)
Conference Location Orlando, FL, USA
Start Date Dec 15, 2021
End Date Dec 18, 2021
Acceptance Date Nov 10, 2021
Publication Date Dec 15, 2021
Deposit Date Dec 2, 2021
Publicly Available Date Dec 3, 2021
Publisher Institute of Electrical and Electronics Engineers
ISBN 9781665445993
Publisher URL


Accepted Conference Proceeding (297 Kb)

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