Using Inter-comment Similarity for Comment Spam Detection in Chinese Blogs

Blog has become one of the most popular ways of communication among social communities since blog posts can be replied, commented, and even shared to other users in a convenient way. All posts and comments, no matter good or bad, have to be manually coordinated by blog owners. In order to prevent comment spam, most blog sites provide challenge-response tests such as CAPTCHA to ensure that the response is from human, instead of automatically generated by a computer. However, these tests cannot prohibit spammers from manually leaving spam messages. Existing studies of Chinese blog comment spam only focus on comments containing hyperlinks, which only stand for a small portion of blog comment spam. In this paper, we propose to include inter-comment Jaccard similarity in the features in addition to the post-comment similarity, stop words ratio, and comment length for blog comment classification. In order to verify the effects of inter-comment similarity features, we compared several classification algorithms such as C4.5, Naïve Bayes, and Neural Network. Experimental results showed that the feature combination of inter-comment and post-comment similarity under the classification of C4.5 achieves the best performance. This shows the effectiveness of the proposed inter-comment similarity feature for Chinese blog comment spam classification.