1. A method comprising: using one or more computer processors to perform operations of: generating, for an online social networking service having members with individual electronic profiles having features, a recommendation graph including a plurality of vertices and a plurality of edges, each vertex representing a member, each edge representing a recommendation of a recommendee member by a recommender member, the recommendation accepted by the recommendee member; training a reputation model to learn a respective importance for each respective feature of a subset of features of the electronic profiles, by providing the generated recommendation graph to a classifier; estimating the professional reputation of a member by applying the trained reputation model to a feature vector of the member, the feature vector including features included in the subset of features, wherein applying includes adjusting a respective feature value in the feature vector of the member by a respective weight corresponding to the respective learned importance of the respective feature; and aggregating a set of estimated professional reputations of members that have engaged with a content item posted on the online social networking service; determining, based on the aggregated set of estimated professional reputations, whether the posted content item is spam content; and generating an updated user interface to include: the posted content item; a first visual indictor of the aggregated set of estimated professional reputations as an aggregated reputation; and a second visual indicator of whether the posted content item is spam content.