Rumour Detection via News Propagation Dynamics and User Representation Learning

Abstract

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.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Distributed Representations of Sentences and Documents
• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Computational fact checking from knowledge networks
• TI-CNN: Convolutional Neural Networks for Fake News Detection
• Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media
• Some Like it Hoax: Automated Fake News Detection in Social Networks
• A Stylometric Inquiry into Hyperpartisan and Fake News
• FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network
• Multiview Deep Learning for Predicting Twitter Users' Location
• Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection