A Theoretical Analysis of Crowdsourced Content Curation

To deal with the huge amount of potentially interesting content on the web today, users seek the help of curators to recommend which content to consume. The two most common forms of curation are expert-based (the editor of a newspaper decides which articles to place on the front page), and algorithmic-based (a search algorithm determines the ranking of websites for a given query). In recent years, content aggregators which use explicit vote-based feedback to curate content for future users have grown exponentially in popularity. The goal of this paper is to provide a descriptive analysis of these crowdsourced curation mechanisms. In particular, we study crowd-curation mechanisms that rank articles according to a score which is a function of userfeedback. We precisely quantify the dynamics of which articles become popular in such these systems. While crowdcuration can be relatively effective for cardinal objectives like discovering and promoting content of high quality, they do not perform well for ordinal objectives such as finding the best articles. Our analysis suggests that user preferences and behavior are a far greater determinant of curation quality than the actual details of the curation mechanism. Finally, we show that certain shifts in user voting behavior can have positive impacts on these systems, suggesting that active moderation of user behavior is important for high quality curation in crowd-sourced systems.