Reading list
The sources behind the journal, numbered in the order I logged them. Journal entries link here.
Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). Generative engine optimization: How to dominate AI search. arXiv. doi.org/10.48550/arXiv.2509.08919
GEO is the perfect example of how marketers influence purchasing decisions at their core. A classmate suggested I could run its method at a smaller scale instead of inventing a new one.
Zhang, D., Li, W., Niu, B., & Wu, C. (2023). A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information. Decision Support Systems, 166, Article 113911. doi.org/10.1016/j.dss.2022.113911
Fake reviewers measurably hurt trust in a platform. That backs my choice of reviews as the 1 variable to isolate.
Capital One Shopping Research. (2026, March 3). Fake review statistics (2026): On Amazon & other websites. capitaloneshopping.com/research/fake-review-statistics
This gives me my headline numbers: roughly 30% of online reviews are fake, at an estimated $770.7 billion in unwanted purchases in 2025.
Luca, M., & Zervas, G. (2016). Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Science, 62(12), 3412-3427. doi.org/10.1287/mnsc.2015.2304
Yelp filtered about 16% of restaurant reviews as suspicious, mostly at struggling businesses or ones with many competitors. Fraud is strategic.
He, S., Hollenbeck, B., & Proserpio, D. (2022). The market for fake reviews. Marketing Science, 41(5), 896-921.
He et al. documented an actual marketplace for fake Amazon reviews run through Facebook groups, with short-lived bumps in ratings and sales.
Federal Trade Commission. (2025, December 22). FTC warns 10 companies about possible violations of the agency's new Consumer Review Rule [Press release]. ftc.gov
Proof the regulators have already responded. The FTC now bans fake, insider, and AI-generated reviews, with penalties up to $53,088 per violation.
Yang, F., Chen, H., Yu, X., Zhang, M., Xiao, X., & Deng, M. (2027). A survey on fake review detection: From pre-trained language models to large language models. Information Fusion, 138, Article 104715. doi.org/10.1016/j.inffus.2026.104715
My map of what already exists: nearly 2 decades of fake-review detection, from classic machine learning to BERT to LLM detectors.
Mozilla. (2025, May 22). Investing in what moves the internet forward. The Mozilla Blog. blog.mozilla.org
Fakespot was the closest tool to my idea. Mozilla shut it down in 2025, probably because scraping at that scale cost too much to keep running.
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011). Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 309-319). aclanthology.org/P11-1032
A classmate pointed me to this labeled deceptive-review corpus. It would let me test the tool on reviews already marked real or fake.
Open-access article shared by my mentor on Sep 16, 2026. doaj.org
Reading for fake review detection. Title and authors go here once I have read it.
Information Fusion article shared by my mentor on Sep 16, 2026, via the ASU library. sciencedirect.com
The second reading for the same task. Likely the same survey I cite as Yang et al. above. If so, the 2 entries merge.