Abstract Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
The techniques used in detecting the opinion fraud vary a lot. Some researchers use classical classification methods such as Support Vector Machine (SVM) @cite_2 , Naive Bayes Classifiers, and logistic regression. Such as employ bag-of-words and bag-of-opinions Latent Dirichlet Allocation (LDA) model to detect singleton review spammers based on semantic similarity, in which they use cosine similarity as a measure, and topics and extracted from the reviews text, with parts-of-speech (POS) as patterns. And others may prefer statistical methods: use unsupervised Bayesian approach and considered user's behavior features and bigrams; A most recent work models the distribution of reviewers' posting rates and utilizes a coupled hidden Markov model to capture both reviewers' posting behaviors and co-bursting signals. There are also some trials on deep learning, which achieves great success in several areas in recent years. Several attempts include Wang's work, which proposes using a hybrid convolutional neural network to detect fake news. Baohua et al designed a semi-supervised recursive autoencoders for detecting review spam in Weibo, a Chinese alternative to Twitter. Still, to the best of our knowledge, very limited work on hybrid deep learning models are proposed for fake opinion detection.
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Consumers increasingly rate, review and research products online. Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90 accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.