Biblio

Comment spam is one of the great challenges faced by forum administrators. Detecting and blocking comment spam can relieve the load on servers, improve user experience and purify the network conditions. This paper focuses on the detection of comment spam. The behaviors of spammer and the content of spam were analyzed. According to analysis results, two types of effective features are extracted which can make a better description of spammer characteristics. Additionally, a gradient boosting tree algorithm was used to construct the comment spam detector based on the extracted features. Our proposed method is examined on a blog spam dataset which was published by previous research, and the result illustrates that our method performs better than the previous method on detection accuracy. Moreover, the CPU time is recorded to demonstrate that the time spent on both training and testing maintains a small value.