Automatic Rumor Detection on Microblogs: A Survey

Abstract

The ever-increasing amount of multimedia content on modern social media platforms are valuable in many applications. While the openness and convenience features of social media also foster many rumors online. Without verification, these rumors would reach thousands of users immediately and cause serious damages. Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques. Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches. In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection. We also give an introduction on existing datasets for rumor detection which would benefit following researches in this area. We give our suggestions for future rumors detection on microblogs as a conclusion.

arXiv commentSubmitted to IEEE TKDE

References in corpus (5)

• Determining the Veracity of Rumours on Twitter
• SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
• Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
• Image Credibility Analysis with Effective Domain Transferred Deep Networks
• On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners

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• Graph Mining for Cybersecurity: A Survey
• The Future of Misinformation Detection: New Perspectives and Trends
• Detecting Rumor Veracity with Only Textual Information by Double-Channel Structure
• The Mass, Fake News, and Cognition Security
• VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text
• Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?