Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms
This addresses the issue of fake reviews impacting consumers and service providers in e-commerce, though it appears incremental as it builds on existing active learning and network-based techniques.
The paper tackles the problem of detecting fake reviews (opinion spam) in e-commerce by proposing a method that combines machine learning with a network-based algorithm to address the scarcity of labeled data, achieving results that outperform state-of-the-art active learning approaches and methods using more labeled data on three large Yelp datasets.
E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are many reasons why it is hard to identify opinion spammers automatically, including the absence of reliable labeled data. This limitation precludes an off-the-shelf application of a machine learning pipeline. We propose a new method for classifying reviewers as spammers or benign, combining machine learning with a message-passing algorithm that capitalizes on the users' graph structure to compensate for the possible scarcity of labeled data. We devise a new way of sampling the labels for the training step (active learning), replacing the typical uniform sampling. Experiments on three large real-world datasets from Yelp.com show that our method outperforms state-of-the-art active learning approaches and also machine learning methods that use a much larger set of labeled data for training.