Detecting Spam And Promotion Campaigns In The Twitter Social Network

The Twitter social network has become a target platform for both promoters and spammers to disseminate their target messages. There are a large number of campaigns containing coordinated spam or promotion accounts in Twitter, which are more harmful than the traditional methods, such as email spamming. Both promotion and spam campaigns can manipulate large amount of accounts to disseminate unwanted and malicious contents in the system and they can infect more normal users than the individual spam accounts. Since traditional solutions mainly check individual accounts or messages, it is an urgent task to detect spam and promotion campaigns in Twitter.In this paper, we propose a scalable framework to detect both spam and promotion campaigns. Our framework consists of three steps:firstly linking accounts who post URLs for similar purposes, secondly extracting candidate campaigns which may exist for spam or promotion purpose and finally distinguishing their intents. One salient aspect of the framework is introducing a URL-driven estimation method to measure the similarity between accounts’ purposes of posting URLs, the other one is proposing multiple features to distinguish the candidate campaigns based on a machine learning method. Over a large-scale dataset from Twitter, we can extract the actual campaigns with high precision and recall and distinguish the majority of the candidate campaigns correctly.