If It Is Funny, It Is Mean: Understanding Social Perceptions of Yelp Online Reviews
Association for Computing Machinery
Online recommendation communities, like Yelp, are valuable information sources for people. Yet, we assert, review communities have their own dynamics behind the social interactions therein. In this work, we study the Yelp review votes of useful, funny, and/or cool to understand these social perceptions of the review. We examine the relationship between these social signals and the emotional valence of the review itself (text and rating). We aim to understand the community's perception of each of these signaling contributions. We construct a conditional inference tree of social signals from 230K Yelp reviews to study how social signals shape the deviance in review rating from the mean rating, an indicator of the overall business rating on Yelp. We find two effects of social signals. First, reviews voted as useful and funny are associated with lower user ratings and relatively negative tone in the review text. Second, reviews voted as cool tend to have a relatively positive tone and higher ratings. Our findings open a research direction for further understanding of perceptions of social signals and have implications for design of recommendation systems.