The current social network has replaced traditional media as an important platform for information exchange. The information in social networks has the advantages of fast dissemination

wide range

and strong immediacy. However

due to the lack of effective supervision means when publishing information

the social network platform has also become a hotbed of rumors. Therefore

the rapid and effective detection of social network rumors is essential for purifying the network environment and maintaining public safety. Firstly

this article explains the definition of rumors

and the problems of current rumors detection and detection process are described. Secondly

different data acquisition methods are introduced and their advantages and disadvantages are analyzed. At the same time

different data annotation methods in rumor detection are compared. Thirdly

according to the development of rumor detection technology

analyze and compare the existing rumors detection methods of artificial

machine learning and deep learning. Fourthly

current mainstream algorithms are empirically evaluated under the same open data set through experiments. Finally

analyze and summarize the challenges faced by current social network rumor detection technology.