Conference proceeding
Multimodal Emergent Fake News Detection via Meta Neural Process Networks
KDD '21: Proceedings of the 27th Acm SIGKDD Conference on Knowledge Discovery & Data Mining, pp.3708-3716
01/01/2021
DOI: 10.1145/3447548.3467153
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.
Details
- Title: Subtitle
- Multimodal Emergent Fake News Detection via Meta Neural Process Networks
- Creators
- Yaqing Wang - Purdue University West LafayetteFenglong Ma - Pennsylvania State UniversityHaoyu Wang - Purdue University West LafayetteKishlay Jha - University of VirginiaJing Gao - Purdue University West Lafayette
- Resource Type
- Conference proceeding
- Publication Details
- KDD '21: Proceedings of the 27th Acm SIGKDD Conference on Knowledge Discovery & Data Mining, pp.3708-3716
- DOI
- 10.1145/3447548.3467153
- Publisher
- Association of Computing Machinery
- Number of pages
- 9
- Grant note
- NSF IIS-1553411; IIS-1956017 / US National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2021
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984294925202771
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