Subjective information or objective information? How does review content contribute to the patients’ review helpfulness in online health communities? (Preprint)

Abstract

Online review has become an important topic in OHCs. However, the factors contributing to review helpfulness remain ambiguous.

To fill this gap, this study examines the effects of review content on review helpfulness in OHCs by focusing on two variables: sentiment and informational support description, based on the Elaboration Likelihood Model. Furthermore, the moderating effect of informational support description is also investigated.

A dataset comprising 210,555 reviews from a prominent OHC platform in China was collected using a self-developed Python web crawler. The review sentiment was analyzed by Baidu AI opening platform and the presence of medical-specific words within a review served as an indicator for measuring the informational support description. Given that review helpfulness is a truncated variable, we employ Tobit regression models to evaluate the effects.

The results indicate that both positive and negative sentiment contribute to review helpfulness. Moreover, there exists a positive association between informational support description and review helpfulness. When affecting review helpfulness, informational support description negatively moderates the impact of sentiment on review helpfulness.

By adopting the ELM as our theoretical framework, this study makes valuable contributions to existing literatures on online reviews within OHCs. The findings can help OHC managers guide patients generating more informative and useful reviews.