  Peer recommendation is a crowdsourcing task that leverages the opinions of
many to identify interesting content online, such as news, images, or videos.
Peer recommendation applications often use social signals, e.g., the number of
prior recommendations, to guide people to the more interesting content. How
people react to social signals, in combination with content quality and its
presentation order, determines the outcomes of peer recommendation, i.e., item
popularity. Using Amazon Mechanical Turk, we experimentally measure the effects
of social signals in peer recommendation. Specifically, after controlling for
variation due to item content and its position, we find that social signals
affect item popularity about half as much as position and content do. These
effects are somewhat correlated, so social signals exacerbate the "rich get
richer" phenomenon, which results in a wider variance of popularity. Further,
social signals change individual preferences, creating a "herding" effect that
biases people's judgments about the content. Despite this, we find that social
signals improve the efficiency of peer recommendation by reducing the effort
devoted to evaluating content while maintaining recommendation quality.
