Identifying Hijacked Reviews

TL;DR

This paper introduces a framework for detecting review hijacking on online marketplaces, using synthetic data and machine learning models, revealing hundreds of previously unknown hijacked reviews in a large dataset.

Contribution

It proposes a novel synthetic data generation method and evaluates machine learning models for identifying hijacked reviews, addressing a gap in existing research.

Findings

Twin LSTM and BERT models effectively distinguish hijacked reviews

Hundreds of new hijacking cases identified in large dataset

Synthetic data generation aids in training detection models

Abstract

Fake reviews and review manipulation are growing problems on online marketplaces globally. Review Hijacking is a new review manipulation tactic in which unethical sellers "hijack" an existing product page (usually one with many positive reviews), then update the product details like title, photo, and description with those of an entirely different product. With the earlier reviews still attached, the new item appears well-reviewed. However, there are no public datasets of review hijacking and little is known in the literature about this tactic. Hence, this paper proposes a three-part study: (i) we propose a framework to generate synthetically labeled data for review hijacking by swapping products and reviews; (ii) then, we evaluate the potential of both a Twin LSTM network and BERT sequence pair classifier to distinguish legitimate reviews from hijacked ones using this data; and (iii) we then deploy the best performing model on a collection of 31K products (with 6.5 M reviews) in the original data, where we find 100s of previously unknown examples of review hijacking.