Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak
  Signals
Amila Silva Ling Luo Shanika Karunasekera Christopher Leckie
The emergence of social media as one of the main platforms for people to
access news has enabled the wide dissemination of fake news. This has motivated
numerous studies on automating fake news detection. Although there have been
limited attempts at unsupervised fake news detection, their performance suffers
due to not exploiting the knowledge from various modalities related to news
records and due to the presence of various latent biases in the existing news
datasets. To address these limitations, this work proposes an effective
framework for unsupervised fake news detection, which first embeds the
knowledge available in four modalities in news records and then proposes a
novel noise-robust self-supervised learning technique to identify the veracity
of news records from the multi-modal embeddings. Also, we propose a novel
technique to construct news datasets minimizing the latent biases in existing
news datasets. Following the proposed approach for dataset construction, we
produce a Large-scale Unlabelled News Dataset consisting 419,351 news articles
related to COVID-19, acronymed as LUND-COVID. We trained the proposed
unsupervised framework using LUND-COVID to exploit the potential of large
datasets, and evaluate it using a set of existing labelled datasets. Our
results show that the proposed unsupervised framework largely outperforms
existing unsupervised baselines for different tasks such as multi-modal fake
news detection, fake news early detection and few-shot fake news detection,
while yielding notable improvements for unseen domains during training.