Rumour Detection via News Propagation Dynamics and User Representation Learning
This addresses the negative impact of rumours on social media users, though it is incremental as it builds on existing methods by incorporating propagation patterns.
The paper tackles the problem of early rumour detection on social media by proposing a deep learning method that leverages news propagation dynamics and user representation learning, achieving state-of-the-art performance on Twitter and Weibo datasets.
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.