# Automatic Rumor Detection on Microblogs: A Survey

arXiv: https://arxiv.org/abs/1807.03505 · HTML: https://mlsanity.com/paper/1807.03505/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 13 of 13)

- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text](https://mlsanity.com/paper/2102.00816/) (2021)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection](https://mlsanity.com/paper/2204.11515/) (2022)
- [Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments](https://mlsanity.com/paper/2502.14383/) (2025)
- [Rumour detection using graph neural network and oversampling in benchmark Twitter dataset](https://mlsanity.com/paper/2212.10080/) (2022)
- [Detecting Rumor Veracity with Only Textual Information by Double-Channel Structure](https://mlsanity.com/paper/2312.03195/) (2023)
- [CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking](https://mlsanity.com/paper/2509.03957/) (2025)
- [Region-enhanced Deep Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2206.07665/) (2022)
- [Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance](https://mlsanity.com/paper/2401.09724/) (2024)

## Abstract (arXiv)

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.