Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms

TL;DR

This paper introduces a novel approach combining machine learning and network-based algorithms to detect opinion spam in online reviews, effectively addressing the challenge of limited labeled data.

Contribution

It proposes a new classification method that leverages user graph structures and active learning to improve spam detection accuracy with scarce labeled data.

Findings

Outperforms existing active learning methods

Requires fewer labeled samples for effective detection

Achieves higher accuracy on real-world datasets

Abstract

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.