Transforming Fake News: Robust Generalisable News Classification Using Transformers
This addresses the problem of fake news detection for online platforms and users, with incremental improvements in generalization and handling of subjective content.
The study tackled fake news classification by exploring transformers' generalization to unseen datasets and proposing a two-step pipeline to remove opinion-based articles, resulting in up to 4.9% F1 score improvement for out-of-distribution generalization and an additional 10.1% increase with the pipeline.
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.