Paper Info
Title
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection.
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.
Year
DOI
Venue
2018
10.1145/3219819.3219903
KDD
Keywords
Field
DocType
Fake news detection,adversarial neural networks,deep learning
Discriminator,Social media,Computer science,Fake news,Extractor,Artificial intelligence,Deep learning,Artificial neural network,Machine learning,Modal,Adversarial system
Conference
ISBN
Citations 
PageRank 
978-1-4503-5552-0
33
1.07
References 
Authors
17
8
Name
Order
Citations
PageRank
Yaqing Wang1959.71
Fenglong Ma237433.08
Zhiwei Jin31699.94
Ye Yuan4732.71
Guangxu Xun510911.89
Kishlay Jha6497.83
lu su7111866.61
Jing Gao82723131.05