Multimodal Emergent Fake News Detection via Meta Neural Process Networks

TL;DR

MetaFEND is a novel meta-learning framework that rapidly adapts to detect fake news on emergent events with minimal labeled data, outperforming existing methods on multimedia social media datasets.

Contribution

The paper introduces MetaFEND, combining meta-learning and neural processes to enable quick adaptation to new fake news detection tasks with limited data.

Findings

MetaFEND outperforms state-of-the-art methods on Twitter and Weibo datasets.

The model effectively detects fake news on unseen emergent events.

Proposed label embedding and attention mechanisms improve detection accuracy.

Abstract

Fake news travels at unprecedented speeds, reaches global audiences and puts users and communities at great risk via social media platforms. Deep learning based models show good performance when trained on large amounts of labeled data on events of interest, whereas the performance of models tends to degrade on other events due to domain shift. Therefore, significant challenges are posed for existing detection approaches to detect fake news on emergent events, where large-scale labeled datasets are difficult to obtain. Moreover, adding the knowledge from newly emergent events requires to build a new model from scratch or continue to fine-tune the model, which can be challenging, expensive, and unrealistic for real-world settings. In order to address those challenges, we propose an end-to-end fake news detection framework named MetaFEND, which is able to learn quickly to detect fake news on emergent events with a few verified posts. Specifically, the proposed model integrates meta-learning and neural process methods together to enjoy the benefits of these approaches. In particular, a label embedding module and a hard attention mechanism are proposed to enhance the effectiveness by handling categorical information and trimming irrelevant posts. Extensive experiments are conducted on multimedia datasets collected from Twitter and Weibo. The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.