Researchers hope study findings help more consumers be vigilant when browsing online
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A new study found that many consumers tend to fall for fake online reviews as a result of truth bias the tendency for people to believe that others are telling the truth, even when there's little or no evidence.
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There arent any glaring warning signs to identify reviews that are fake versus those that are real.
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Experts encourage review platforms rather than consumers to take on the responsibility of combating fake reviews.
A new study conducted by researchers from the University of South Florida explored the reasons why many consumers fall for fake online reviews.
Their work found that when people see an online review that seems fake, they tend to ignore itunless theres solid proof its false.
In other words, consumers might be suspicious, but they wont fully distrust a review without clear evidence. The researchers say this shows just how tricky it can be to fight fake reviews, especially when most shoppers still give them the benefit of the doubt.
ConsumerAffairs interviewed researcher Dezhi Yin to break down these findings further, including why consumers continue to fall for fake reviews, how to spot fake reviews, combating truth bias, and more.
Why do consumers fall for fake reviews?
A major reason consumers fall for fake reviews is their general tendency to trust reviews treating reviews (mostly from strangers) as real by default, Yin explained. This tendency is called truth bias. This has some evolutionary basis our ancestors had to cooperate with each other (and even strangers) to survive, and trusting (rather than suspecting) others by default is necessary for large-scale cooperation to occur.
We conducted five experiments, and they provided very consistent evidence that consumers have a strong truth bias by default (especially for negative reviews). In one experiment, participants were shown 20 restaurant reviews and told that only 10 of the reviews were authentic. All the reviews were presented on a single screen, making it easy for participants to go back and forth to calibrate their judgments. Nonetheless, they still classified an average of 11.38 reviews as authentic.
Can you identify fake reviews?
According to Yin, it can be difficult for many consumers to be able to spot the real reviews from the fake.
Based on the truth default theory, there are likely no reliable signals that can tell a fake review from real ones. The reason is that many people are good at pretending or faking (when they are given sufficient time), and they can produce fabricated content that looks real.
On the other hand, some truth-tellers may appear nervous or exhibit hesitant body gestures because of their personalities, etc., even when they are telling the truth.
Yin also shared some examples of this at work in the teams most recent clinical study:
In our research, we conducted a pretest, asking participants to recall an actual dining experience, and then write a real review or a fake review for the restaurant (opposite to their actual experience). Many of such fake reviews are high quality and barely distinguishable from real reviews. In the meantime, some real reviews are low in quality, with typos, etc., that make those reviews appear to be fake.
How to combat fake reviews
The consequences of falling for fake reviews are felt solely by consumers. However, Yin believes its the responsibility of review platforms to make the experience more accurate for users.
Among the myriad approaches currently employed by review platforms to identify and deal with fake reviews proactively, some approaches rely on readers themselves. Google Reviews allows users to report suspected fakes as either spam or conflicts of interest, he explained. Our findings strongly suggest that such reporting is unlikely to be effective and should be supplemented with other approaches.
Because truth bias is more pronounced for negative reviews, fake negative reviews (of competitors) in the hands of malicious actors in the marketplace are particularly damaging for targeted businesses. Therefore, it is reasonable for review platforms to prioritize detecting fake negative reviews over detecting fake positive reviews.
Posted: 2025-06-12 17:54:21
