When the Crowd is Not Enough

Abstract

Social media gives voice to the people, but also opens the door to low-quality contributions, which degrade the experience for the majority of users. To address the latter issue, the prevailing solution is to rely on the 'wisdom of the crowds' to promote good content (e.g., via votes or 'like' buttons), or to downgrade bad content. Unfortunately, such crowd feedback may be sparse, subjective, and slow to accumulate. In this pa- per, we investigate the effects, on the users, of automatically filtering question-answering content, using a combination of syntactic, semantic, and social signals. Using this filtering, a large-scale experiment with real users was performed to mea- sure the resulting engagement and satisfaction. To our knowledge, this experiment represents the first reported large-scale user study of automatically curating social media content in real time. Our results show that automated quality filtering indeed improves user engagement, usually aligning with, and often outperforming, crowd-based quality judgments.