Automated crowdturfing attack in Chinese user reviews - Li’na WANG; Xiaodong GUO; Run WANG (Tongxin xuebao)
The text-oriented automated crowdturfing attack has a series of features such as low attack cost and strong concealment.This kind of attack can automatically generate a large number of fake reviews,with harmful effect on the healthy development of the user review community.In recent years,researchers have found that text-oriented crowdturfing attacks for the English review community,but there was few research work on automated crowdsourcing attacks in the Chinese review community.A Chinese character embedding LSTM model was proposed to automatically generate Chinese reviews with the aim of antomated crowdturfing attacks,which model trained by a combination with Chinese character embedding network,LSTM network and softmax dense network,and a temperature parameter T was designed to construct the attack model.In the experiment,more than 50 000 real user reviews were crawled from Taobao's online review platform to verify the effectiveness of the attack method.Experimental results show that the generated fake reviews can effectively fool linguistics-based classification detection approach and texts plagiarism detection approach.Besides,the massive manually evaluation experiments also demonstrate that the generated reviews with the proposed attack approach perform well in reality and diversity.