Rumour Detection via News Propagation Dynamics and User Representation Learning

• Main Authors: Do, Tien Huu, Luo, Xiao, Nguyen, Duc Minh, Deligiannis, Nikos
• Format: Journal Article
• Language: English
• Published: 18-04-2019
• Subjects: Computer Science - Computation and Language; Computer Science - Learning; Computer Science - Social and Information Networks; Statistics - Machine Learning
• DOI: 10.48550/arxiv.1905.03042

Rumours have existed for a long time and have been known for serious
consequences. The rapid growth of social media platforms has multiplied the
negative impact of rumours; it thus becomes important to early detect them.
Many methods have been introduced to detect rumours using the content or the
social context of news. However, most existing methods ignore or do not explore
effectively the propagation pattern of news in social media, including the
sequence of interactions of social media users with news across time. In this
work, we propose a novel method for rumour detection based on deep learning.
Our method leverages the propagation process of the news by learning the users'
representation and the temporal interrelation of users' responses. Experiments
conducted on Twitter and Weibo datasets demonstrate the state-of-the-art
performance of the proposed method.