Detecting "Smart" Spammers On Social Network:A Topic Model Approach
Table 1: Performance comparisons for our features with three baseline classifiers
['Feature', 'Method', 'Weibo Dataset Precision', 'Weibo Dataset Recall', 'Weibo Dataset F1-score', 'Honeypot Dataset Precision', 'Honeypot Dataset Recall', 'Honeypot Dataset F1-score']
Three baseline classification methods: Support Vector Machines (SVM), Adaboost, and Random Forests are adopted to evaluate our extracted features. On each dataset, the employed classifiers are trained with individual feature first, and then with the combination of the two features. Besides, the classification by combination of LOSS and GOSS can increase accuracy by more than 3% compared with raw topic distribution probability.