Upvotes, Downvotes, and Initial Conditions: An Analysis of Reddit’s Curation Mechanisms
NOTE: THIS TALK IS FROM 1pm - 2pm. Website difficulties are causing the time to be off.
In recent years, the social news site Reddit.com has grown exponentially in popularity as more and more people use it to find good content from around the web. According to Alexa estimates, Reddit is the 84th most popular website and receives almost as many daily visitors as the New York Times’ website or Google News. Reddit operates according to a simple set of rules: some users submit links to articles, pictures, etc and other users vote those submissions up or down. Articles are ranked based on the number of upvotes and downvotes received from the community, as well as the time of their submission. While it is often said that this mechanism allows the best content to rise to the top, it is not clear this is actually true. The goal of this paper is to interrogate this claim, and more generally, to characterize the interactions between Reddit’s mechanisms and the behavior of the crowd of people who use Reddit.
This work demonstrates that the articles at the top of Reddit’s ranking are not necessarily the best submissions. In fact, only a small percentage of the best submissions will make it to the top. This phenomena is driven by the behavior that most users only view and vote on articles at the top of the ranking, while a very small fraction of users view content lower down in the ranking. This paper proves the disparity in attention limits Reddit’s ability to curate effectively. We introduce a model which captures the essential features of Reddit and precisely characterize the dynamics of popularity and severity of this effect through a combination of theoretical results and simulations. We further show there is significant qualitative agreement between our simulations and empirical observations of Reddit. Based on our simulations, it could be that as much as 80% of the best content on Reddit is going unseen by most members of the community.
Bio: Greg Stoddard is a 4th year PhD student in the computer science department at Northwestern University and is currently a visiting student at Harvard University. His research focuses on designing and analyzing social computing systems using tools from game theory and algorithm design.
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