Conference proceeding
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
KDD'18: Proceedings of the 24th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining, pp.849-857
01/01/2018
DOI: 10.1145/3219819.3219903
Abstract
As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can cause negative effects or even manipulate the public events. One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. Unfortunately, most of the existing approaches can hardly handle this challenge, since they tend to learn event-specific features that can not be transferred to unseen events. In order to address this issue, we propose an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events. It consists of three main components: the multi-modal feature extractor, the fake news detector, and the event discriminator. The multi-modal feature extractor is responsible for extracting the textual and visual features from posts. It cooperates with the fake news detector to learn the discriminable representation for the detection of fake news. The role of event discriminator is to remove the event-specific features and keep shared features among events. Extensive experiments are conducted on multimedia datasets collected from Weibo and Twitter. The experimental results show our proposed EANN model can outperform the state-of-the-art methods, and learn transferable feature representations.
Details
- Title: Subtitle
- EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
- Creators
- Yaqing Wang - University at Buffalo, State University of New YorkFenglong Ma - University at Buffalo, State University of New YorkZhiwei Jin - University of Chinese Academy of SciencesYe Yuan - Beijing University of TechnologyGuangxu Xun - University at Buffalo, State University of New YorkKishlay Jha - University at Buffalo, State University of New YorkLu Su - University at Buffalo, State University of New YorkJing Gao - University at Buffalo, State University of New YorkACM
- Resource Type
- Conference proceeding
- Publication Details
- KDD'18: Proceedings of the 24th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining, pp.849-857
- DOI
- 10.1145/3219819.3219903
- Publisher
- Assoc Computing Machinery
- Number of pages
- 9
- Grant note
- CNS-1742845 / US National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984294925302771
Metrics
47 Record Views