Detecting Fake Reviews with Generative Adversarial Networks for Mobile Social Networks

Table 1

The comparison results of different models.

Methods	Precision	Recall	F1-score	AUC
LR (TF-IDF)	0.49	0.59	0.53	0.55
XGBoost (word2vec + TextCNN)	0.63	0.64	0.63	0.66
AdaBoost (word2vec + TextCNN)	0.70	0.59	0.64	0.62
Random forest (word2vec + TextCNN)	0.76	0.77	0.77	0.76
SVM (TF-IDF)	0.74	0.71	0.73	0.82
KNN (TF-IDF)	0.73	0.75	0.74	0.66
TFM	0.66	0.68	0.67	0.79
Zhang et al.’s method [31]	0.70	0.68	0.69	0.79
Lyu et al.’s method [16]	0.72	0.74	0.73	0.80
Our model (word2vec + TextCNN + GANs)	0.79	0.80	0.80	0.82