An improved social spammer detection based on tri-training

A social spammer detection model based on tri-training (SSDTT) is adopted. The main procedure of the work is: First, train three original classifiers with a small amount of labeled data. Then, select confident users that are labeled for a classifier if the other two classifiers agree on the labeling as new training data. Afterwards, repeat these steps until three classifiers are not updated. Experimental results indicate that SSDTT has the same performance with the supervised learning in the case of lacking sufficient labeled data.