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

arXiv: https://arxiv.org/abs/2205.13422 · HTML: https://mlsanity.com/paper/2205.13422/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media](https://mlsanity.com/paper/1703.03609/) (2017)
- [DFraud3- Multi-Component Fraud Detection freeof Cold-start](https://mlsanity.com/paper/2006.05718/) (2020)
- [ColluEagle: Collusive review spammer detection using Markov random fields](https://mlsanity.com/paper/1911.01690/) (2019)

## Built on by (most cited first, 1 of 1)

- [Signed Latent Factors for Spamming Activity Detection](https://mlsanity.com/paper/2209.13814/) (2022)

## Abstract (arXiv)

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.