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how to remove amazon feedback
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the study used a dataset of 1600 reviews of chicago hotels. some of them were real; others were fake. the reviews were presented to 400 subjects: each subject got eight reviews to read: a balanced set of two positive fake, two positive real, two negative fake and two negative real, presented in a random order. the reviews were written by real people who were given information about the hotel and told to work how to remove amazon feedback into the review. when asked why they thought a bad review was trustworthy, participants revealed that they assumed that a negative diatribe about a company would be more truthful than a glowing endorsement. they didn't fully account for the possibility that the reviewer might want to harm the business. the results were illuminating, showing that consumers generally trust negative reviews more than positive ones. moreover, we humans appear to have a bias towards assuming that positive reviews might be fake, azimi says. azimi says they started from a point that had already been well established by previous studies, that we humans are generally not good at detecting deception. and that's when we're face-to-face with each other. trying to tell when we're being conned is even more difficult when we're using text. azimi, chan and krasnikov's study suggests that we're no better with text than we are in person, although the liar's tools may be different when he or she is writing, as opposed to talking. when how to remove amazon feedback came to faking a review, length was important to believability, as was detail. a long, negative review of a hotel, complete with lots of information, tended to convince participants. a lengthy, positive review, on the other hand, was regarded as suspicious, and participants tended to trust writers that kept their glowing reviews short. fake reviews checker online