Multi-modal rumour detection using bilinear pooling and domain adversarial neural networks
by Chao Wang; Hongwei Zhang; Jinrui Zhang; Lichuan Gu
International Journal of Security and Networks (IJSN), Vol. 18, No. 3, 2023

Abstract: Rapid development in the internet era has made posting and obtaining information easier, leading to a sharp increase in rumour numbers. The images are more deceptive than traditional text rumours, making sources and authenticity hard to verify. Therefore, online rumours combining texts and images are more harmful. Detection of multi-modal rumours has become a new challenge. However, most existing methods are difficult to solve this problem and only adopt the standard concatenation for achieving feature fusion among different modes. Accordingly, the fused rumour features can barely effectively capture complementarity and difference among multi-modal data. This study aimed to propose an end-to-end model, named multi-modal rumour detection using bilinear pooling (BL) and domain adversarial neural networks (BPDANN), which adopts BL for multi-modal feature fusion to complement with the other. Further, the event classification module was designed to remove event-specific features and maintain shared features between events based on domain adversarial neural networks. Two text feature extraction methods and two BL methods were combined in pairs for multi-modal feature fusion to verify the effectiveness of BPDANN. Finally, the evaluation was conducted on two public multi-modal rumour datasets, Weibo and Twitter. The results exhibited that BPDANN outperformed current state-of-the-art methods.

Online publication date: Wed, 11-Oct-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Security and Networks (IJSN):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com