The ability to generate faux user generated content at will, with the appearance of having it originate from disparate sources and locales, is difficult to stifle. By nature, FROs are exceptionally good at hiding and one can assume that contingencies have been planned in the event of satellite operations being compromised.
Conventional methods to track FROs like blacklisting reviews originating from common IP address, a technique borrowed from email anti-spam filters, are too simplistic, catching only those least likely to have the ability to launch large scale campaigns capable of materially impacting a hotel’s online reputation.
Circumstantial evidence such as ratios for reviews to the number of occupied rooms or the ratio of frequent reviewers to anonymous reviewers may hint at atypical levels of guest engagement. Additionally, flurries of positive reviews following posting of a negative review may appear unnatural.
Jumping to the conclusion that such flags are evidence of malfeasance is inadvisable. They may indicate a hotel is doing an excellent job of legitimately engaging its community of guests. Or, it just may be a coincidence.
Without being able to prove beyond a reasonable doubt that the review source was illegitimate, protests are likely to fall on deaf ears when brought to the attention of the review site or authorities.
Researchers at Cornell University claim to have developed algorithms that isolate fake reviews based on sentence structure and word utilization. While the research methodology identified certain patterns, the sources of the fake reviews were not professional FROs determined to blend in with the crowd.
How to combat Fake Review Optimization on travel Sites
