This past Thursday 12, we had really interesting talk by Gael Le Mens.
In the presentation, I will give a quick summary of the theory and explain how I used a large dataset of online ratings (all the ratings of all the restaurants in San Francisco since 2004 on the website Yelp.com) to test the assumptions of the model and measure the extent to which a sampling account can explain the association between popularity and quality estimates. Time permitting, I will discuss related data analyses pertaining to the rating behavior of online user communities.
People often evaluate popular alternatives more positively than unpopular alternatives. This has been attributed to inferences about quality on the basis of popularity, motivated cognition or mere exposure. In this paper, we propose an alternative explanation for the evaluative advantage of popular alternatives. Our theory emphasizes the role of the information samples people have about popular and unpopular alternatives. Under the assumption that, after a poor experience, people are more likely to sample again popular alternatives than unpopular alternatives, we show that systematic information biases will emerge. This information bias frequently provides popular alternatives with an evaluative advantage as compared to unpopular alternatives. Our sampling-based account complements existing explanations that focus on how people process information about popular and unpopular alternatives.