Cornell University researchers are developing computer software that can identify fake hotel reviews.
The software is designed to spot what the university refers to as “opinion spam” —phony positive reviews created by sellers to help sell their products, or negative reviews meant to downgrade competitors.
In a test on 800 reviews of Chicago hotels, a computer was able to pick out deceptive reviews with almost 90 percent accuracy.
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In the process, the researchers discovered an intriguing correspondence between the linguistic structure of deceptive reviews and fiction writing.
Graduate student Myle Ott said of the project: “While this is the first study of its kind, and there's a lot more to be done, I think our approach will eventually help review sites identify and eliminate these fraudulent reviews”.
The researchers created what they believe to be the first "gold standard" collection of opinion spam by asking a group of people to deliberately write false positive reviews of 20 Chicago hotels. These were compared with an equal number of carefully verifed truthful reviews.
Using computer analysis based on subtle features of text, the researchers trained a computer on a subset of true and false reviews, then tested it against the rest of the database. The best results, they found, came from combining keyword analysis with the ways certain words are combined in pairs. Adding these two scores identified deceptive reviews with 89.8 percent accuracy.
The research project is in its early stages and currently only applies to hotels in Chicago.
This sort of software might be used by review sites as a “first-round filter,” Ott suggested.
“Ultimately, cutting down on deception helps everyone,” Ott said. “Customers need to be able to trust the reviews they read, and sellers need feedback on how best to improve their services.”
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