Consumers overwhelmingly trust online reviews even when they know many are fake, according to the latest research.
A study, from the University of South Florida, reveals people have a natural bias towards believing information is truthful.
The findings highlight the challenge facing online platforms as they try to maintain trust in review systems while combating increasingly sophisticated fake review operations.
The researchers looked at how consumers judge whether online reviews are real or fake through five experiments between 2018 and 2023.
They found that participants consistently classified most reviews as authentic, even when told in advance that half were fabricated.
"Our research is among the first to examine how consumers make real or fake judgments of online reviews," said study co-author Dezhi Yin, an associate professor at the University of South Florida Muma College of Business.
In one experiment, participants were shown 20 restaurant reviews and told only ten were authentic.
Despite being able to compare all reviews on a single screen, participants classified an average of 11.38 reviews as real, more than the actual number of authentic reviews.
The research identifies what psychologists call "truth bias" the tendency to assume information is truthful unless there is strong evidence otherwise.
This bias helps explain why fake reviews remain effective despite growing public awareness of the problem.
"This illustrates the power of truth bias in this context," Yin said.
The study also found that consumers are more likely to trust negative reviews than positive ones, even though real-world data shows the negative ones are more likely to be fake than the positive.
"Our findings suggest a striking contrast between reality and perception," Yin said.
The research, published in the journal Information Systems Research, has implications for platforms and marketplaces that depend on consumer reviews.
The authors argue that relying on users to report suspicious content is largely ineffective.
Instead, platforms should focus on identifying and removing fake negative reviews and labelling potentially fraudulent content. The researchers suggest interface design changes such as grouping positive and negative reviews separately or providing rating-based sorting tools.
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