V iral products and ideas are intuitively understood to grow through a person-to-person diffusion process analogous to the spread of an infectious disease; however, until recently it has been prohibitively difficult to directly observe purportedly viral events, and thus to rigorously quantify or characterize their structural properties. Here we propose a formal measure of what we label "structural virality" that interpolates between two conceptual extremes: content that gains its popularity through a single, large broadcast and that which grows through multiple generations with any one individual directly responsible for only a fraction of the total adoption. We use this notion of structural virality to analyze a unique data set of a billion diffusion events on Twitter, including the propagation of news stories, videos, images, and petitions. We find that across all domains and all sizes of events, online diffusion is characterized by surprising structural diversity; that is, popular events regularly grow via both broadcast and viral mechanisms, as well as essentially all conceivable combinations of the two. Nevertheless, we find that structural virality is typically low, and remains so independent of size, suggesting that popularity is largely driven by the size of the largest broadcast. Finally, we attempt to replicate these findings with a model of contagion characterized by a low infection rate spreading on a scale-free network. We find that although several of our empirical findings are consistent with such a model, it fails to replicate the observed diversity of structural virality, thereby suggesting new directions for future modeling efforts.

The Web has enabled one of the most visible recent developments in education-the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood.In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high-and low-achieving students, and investigate how forum participation relates to other parts of the course.We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across subpopulations. We find that making badges more salient produced increases in forum engagement.

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An increasingly common feature of online communities and social media sites is a mechanism for rewarding user achievements based on a system of badges. Badges are given to users for particular contributions to a site, such as performing a certain number of actions of a given type. They have been employed in many domains, including news sites like the Huffington Post, educational sites like Khan Academy, and knowledge-creation sites like Wikipedia and Stack Overflow. At the most basic level, badges serve as a summary of a user's key accomplishments; however, experience with these sites also shows that users will put in non-trivial amounts of work to achieve particular badges, and as such, badges can act as powerful incentives. Thus far, however, the incentive structures created by badges have not been well understood, making it difficult to deploy badges with an eye toward the incentives they are likely to create.In this paper, we study how badges can influence and steer user behavior on a site-leading both to increased participation and to changes in the mix of activities a user pursues on the site. We introduce a formal model for reasoning about user behavior in the presence of badges, and in particular for analyzing the ways in which badges can steer users to change their behavior. To evaluate the main predictions of our model, we study the use of badges and their effects on the widely used Stack Overflow question-answering site, and find evidence that their badges steer behavior in ways closely consistent with the predictions of our model. Finally, we investigate the problem of how to optimally place badges in order to induce particular user behaviors. Several robust design principles emerge from our framework that could potentially aid in the design of incentives for a broad range of sites.

We study the patterns by which a user consumes the same item repeatedly over time, in a wide variety domains ranging from checkins at the same business location to re-watches of the same video. We find that recency of consumption is the strongest predictor of repeat consumption. Based on this, we develop a model by which the item from t timesteps ago is reconsumed with a probability proportional to a function of t. We study theoretical properties of this model, develop algorithms to learn reconsumption likelihood as a function of t, and show a strong fit of the resulting inferred function via a power law with exponential cutoff. We then introduce a notion of item quality, show that it alone underperforms our recency-based model, and develop a hybrid model that predicts user choice based on a combination of recency and quality. We show how the parameters of this model may be jointly estimated, and show that the resulting scheme outperforms other alternatives.