Abstract：Microblogging sites are one of the most popular information sharing platforms today. However, among the large amount of posted published every day, spam texts are seen everywhere: users utilize spam posts to advertise, broadcast, boast their own products, and defame their competitors. Therefore, filtering spam tweets is a critical and fundamental problem. In this paper, we propose a semi-supervised algorithm based on Expectation Maximization and Naive Bayesian Classifier (EM-NB), which is able to filter spam tweets effectively using only a small amount of labeled data. The experimental results on more than 140 thousand tweets from Sina Weibo show that our method achieves higher accuracy and F-score than baselines.
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2016, Vol. 30 
