GraphSAGE-Based Spammer Detection Using Social Attribute Relationship

Spammers have existed since the birth of the Internet. They constantly pollute the social network environment, seriously degrade user experience and pose a threat to user account security. Finding spammers has become one of the most important tasks for social networking platforms. However, spammers use various methods to hide themselves from normal users, which makes it more difficult to detect spammers effectively. We propose a spammer detection method based on GraphSAGE Graph Neural Network, which distinguishes spammers from normal users based on the social attribute relationship of accounts. Even if spammers constantly change the content of their spam messages to avoid detection, they can still be identified by the different social attributes of spammers and normal users. In our method, user feature, relationship feature and behavior feature are designed and extracted to represent the social attribute relationship of users. At the same time, we have successfully and effectively utilized GraphSAGE to address the spammer detection problem. We prove the effectiveness of our method through experiments on the real-world dataset, and the results show that our performance is better than other comparison methods.