Words Meet Photos: When and Why Photos Increase Review Helpfulness

Abstract

Are reviews with photos more helpful? If so, do consumers find reviews more helpful when photos and text convey similar or different information? This paper examines the effect of content similarity between photos and text on review helpfulness and its underlying mechanism. Using a dataset of 7.4M reviews associated with 3.5M photos from Yelp, and applying machine learning algorithms, we quantify the similarity of the content between text and photos. We find that, overall, photos increase the helpfulness of a review. More importantly, though, greater similarity between photos and text heightens review helpfulness more. We then validate algorithm-based similarity assessments with similarity perceptions of human judges. Using real-world reviews from Yelp and carefully designed stimuli, we replicate our core findings in five laboratory experiments. Further, testing the underlying mechanism, we find that greater similarity facilitates ease with which consumers can process the review which, in turn, increases that review's helpfulness to consumers. Finally, we show that factors that impede processing ease (e.g., language difficulty or poor image quality) can reduce the effect of similarity on helpfulness. These findings provide novel insights into the value of user-generated content that includes text and photos and its underlying mechanism.

Keywords: online reviews; helpfulness; machine learning; images