Deceptive Spam Research Articles

LDA_RAD: A Spam review detection method based on topic model and reviewer anomaly degree

Online shopping has become a popular activity among consumers. Reviews have become important standards for consumers to buy things. To detect the spam re-views that do not correctly response the right information of goods, a method based on topic model and reviewer anomaly degree is raised. This method divides spam reviews into content-type and deceptive spam review respectively. Firstly, the experimental data set is modelled by LDA topic, which detects the content-type spam reviews with different themes. Then the deceptive spam reviews are detected by the reviewer’s abnormality degree index. It assigns a score to each review according to extracted features and related weights. A score is finally combined with an adaptive weight calculation based on the abnormal period and the reviewer’s similarity to obtain the review score. The review with a high score is considered to be a spam review, and the review with a low score is a true review. Experiments show that this method has a certain improvement on the recognition rate of spam reviews.

Document representation and feature combination for deceptive spam review detection

Deceptive spam reviews of products or service are harmful for customers in decision making. Existing approaches to detect deceptive spam reviews are concerned in feature designing. Hand-crafted features can show some linguistic phenomena, however can hardly reveal the latent semantic meaning of the review. We present a neural network based model to learn the representation of reviews. The model makes a hard attention through the composition from sentence representation into document representation. Specifically, we compute the importance weights of each sentence and incorporate them into the composition process of document representation. In the mixed-domain detection experiment, the results verify the effectiveness of our model by comparing with other neural network based methods. As the feature selection is very important in this direction, we make a feature combination to enhance the performance. Then we get 86.1% F1 value which outperform the state-of-the-art method. In the cross-domain detection experiment, our method has better robustness.