Multimodal Emergent Fake News Detection via Meta Neural Process Networks

Yaqing Wang, Fenglong Ma, Haoyu Wang, Kishlay Jha, Jing Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages3708-3716
Number of pages9
ISBN (Electronic)9781450383325
DOIs
StatePublished - Aug 14 2021
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: Aug 14 2021Aug 18 2021

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/TerritorySingapore
CityVirtual, Online
Period8/14/218/18/21

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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