Professur für Data Science & Digitalisierung

An overwhelming majority of previous works find longer reviews to be more helpful than short reviews. In this study, we propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the line of argumentation. To test this idea, we use a large dataset of customer reviews from Amazon in combination with a state-of-the-art approach from natural language processing that allows us to study argumentation lines at sentence level. Our results disprove the prevailing narrative that longer reviews are uniformly perceived as more helpful and allow retailer platforms to feature more useful product reviews.