Identifying Hijacked Reviews

2021-07-07 · ACL (ECNLP) 2021 8 · Monika Daryani, James Caverlee

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.

📄 PDF Abstract BibTeX arXiv:2107.05385

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Identifying Humor in Reviews using Background Text Sources

2017-09-01 · EMNLP 2017 9 · Alex Morales, ChengXiang Zhai

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Hijacking Context in Large Multi-modal Models

2023-12-07 · JoonHyun Jeong

Recently, Large Multi-modal Models (LMMs) have demonstrated their ability to understand the visual contents of images given the instructions regarding the images. Built upon the Large Language Models (LLMs), LMMs also in…

In-Context Learning

Contrastive Learning

Identifying Hijacked Reviews

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