Detecting Spam and Promoting Campaigns in the Twitter Social Network

The Twitter social network has become a target platform for both promoters and stammers to disseminate their target messages. There are a large number of campaigns containing coordinated spam or promoting accounts in Twitter, which are more harmful than the traditional methods, such as email spamming. Since traditional solutions mainly check individual accounts or messages, it is an urgent task to detect spam and promoting campaigns in Twitter. In this paper, we propose a scalable framework to detect both spam and promoting 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 promoting 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.