Detecting Spam Community Using Retweeting Relationships – A Study on Sina Microblog

Abstract

Microblog marketing is a new trend in social media. Spammers have been increasingly targeting such platforms to disseminate spam and promoting messages. Unlike the past behaviors on traditional media, they connect and support each other to perform spam tasks on microblogs. Therefore existing methods can’t be directly used for detecting spam community. In this paper, we examine the behaviors of spammers on Sina microblog, and obtain some observations about their activities rules. Then we extract content features from tweet text and behavior features from retweeting interactions, perform machine learning to build classification models and identify spammers on microblogs. We evaluate our generated feature set used for detecting spammers under three classification methods, including Naive Bayes, Decision Tree and SVM. Extensive experiments show that our proposed feature set can make the classifiers perform well, and the crawler program combining the SVM classifier can effectively detect spam community.