Abstract
This study aims to identify factors that influence the usefulness of online healthcare reviews and to develop a predictive model for review usefulness. A sample of 4,351 online reviews posted between October 2014 and October 2022 was analyzed using negative binomial regression and support vector regression algorithms. The results reveal that user metadata attributes related to reviewer reputation, readability, subjectivity, and containing more sentences have a significant positive influence on review helpfulness. However, reviews assigning higher star ratings to a business are perceived as less useful by healthcare consumers. The study recommends that healthcare businesses should encourage consumers to post reviews, pay attention to the opinions and concerns of high-reputation and cool patients, and use review, business, and user metadata to build effective models for predicting review usefulness. By using a predictive model like the one developed in this study, online review platforms can estimate the helpfulness of new reviews instantly.
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