Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs
Institute of Computing Technology · University of Chinese Academy of Sciences · +1 more institution
Abstract
Microblogs have become popular media for news propagation in recent years. Meanwhile, numerous rumors and fake news also bloom and spread wildly on the open social media platforms. Without verification, they could seriously jeopardize the credibility of microblogs. We observe that an increasing number of users are using images and videos to post news in addition to texts. Tweets or microblogs are commonly composed of text, image and social context. In this paper, we propose a novel Recurrent Neural Network with an attention mechanism (att-RNN) to fuse multimodal features for effective rumor detection. In this end-to-end network, image features are incorporated into the joint features of text and social…
Citation impact
- FWCI
- 73.68
- Percentile
- 100%
- References
- 32
Authors
5- ZJZhiwei JinCorresponding
Institute of Computing Technology, University of Chinese Academy of Sciences
- JCJuan Cao
University of Chinese Academy of Sciences, Institute of Computing Technology
- HGHan Guo
Institute of Computing Technology, University of Chinese Academy of Sciences
- YZYongdong Zhang
University of Chinese Academy of Sciences, Institute of Computing Technology
- JLJiebo Luo
University of Rochester
Topics & keywords
- Microblogging
- Rumor
- Computer science
- Social media
- Recurrent neural network
- Context (archaeology)
- Artificial intelligence
- Credibility