Exploring extremity and negativity biases in online reviews: Evidence from Yelp. com

Abstract

While some online reviews explicitly praise or criticize a product, others reveal a neutral evaluation. We predicted that extreme reviews would be considered more useful than moderate ones, and that negative reviews would be considered more useful than positive ones. To test these predictions, this study collected a dataset comprising 951,178 reviews of New York restaurants made by 142,286 reviewers on Yelp.com. By combining these two datasets, we incorporated each reviewer’s unique reference point into a model and showed that extremely positive or negative reviews were considered more useful than moderate ones and that negative reviews were considered more useful than positive ones. This dominance of negative over positive reviews was also more pronounced in the conditions of larger variance and lower average ratings for restaurants. Overall, these results support the presence and influence of extremity and negativity biases, particularly in the context of high preference heterogeneity.