Examining the Impacts of Airbnb’s Review Policy Change on Listing Reviews
Abstract
In July 2014, Airbnb, one of the biggest firms in the sharing economy, decided to change the way the guest and the host review each other on the platform. Before this change, the guest/ host could post reviews about their experiences asynchronously- the guest/ host would be able to see the other party’s review whenever it was posted. The new review policy though, rolled out a simultaneous review system in which reviews are viewable only after both the guest/ host post their own reviews. This study empirically evaluates the impacts of this new review policy on guest reviews’ informativeness, measured by both the informational content (semantic diversity and objectivity) and the personal opinions (sentiment and sentiment heterogeneity).
Using Regression Discontinuity Design and a variety of techniques in the text analytics domain, we demonstrate that Airbnb’s review policy change enhanced guest reviews’ informational content in terms of semantic diversity and objectivity. We also show that the reviews’ sentiment deflated while became more diverse. Subgroup analysis revealed that lower quality listings were subject to more changes than did high quality listings. We further explore short-term and long-term effects of the review policy change, and demonstrate that the simultaneous review system has a long-lasting impact on the guest reviews’ informativeness.