  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.
