Generating Behavior Features for Cold-Start Spam Review Detection

Existing studies on spam detection show that behavior features are effective in distinguishing spam and legitimate reviews. However, it usually takes a long time to collect such features and is hard to apply them to cold-start spam review detection tasks. In this paper, we exploit the generative adversarial network for addressing this problem. The key idea is to generate synthetic behavior features (SBFs) for new users from their easily accessible features (EAFs). We conduct extensive experiments on two Yelp datasets. Experimental results demonstrate that our proposed framework significantly outperforms the state-of-the-art methods.