  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.
