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Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook's algorithmically ranked News Feed and further studied users' choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals' choices played a stronger role in limiting exposure to cross-cutting content.

To explore the effects of different incentives on crowdsourcing participation and submission quality, we conduct a randomized field experiment on Taskcn, a large Chinese crowdsourcing site using mechanisms with features of an all-pay auction. In our study, we systematically vary the size of the reward as well as the presence of a soft reserve, or early high-quality submission. We find that a higher reward induces significantly more submissions and submissions of higher quality. In comparison, we find that high-quality users are significantly less likely to enter tasks where a high-quality solution has already been submitted, resulting in lower overall quality in subsequent submissions in such soft reserve treatments. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1845 . This paper was accepted by Uri Gneezy, behavioral economics.

In the classic “influence-maximization” (IM) problem, people influence one another to adopt a product and the goal is to identify people to “seed” with the product so as to maximize long-term adoption. Many influence-maximization models suggest that, if the number of people who can be seeded is unconstrained, then it is optimal to seed everyone at the start of the IM process. In a recent paper, we argued that this is not necessarily the case for social products that people use to communicate with their friends (Iyer and Adamic, The costs of overambitious seeding of social products. In: International Workshop on Complex Networks and Their Applications_273–286, 2018). Through simulations of a model in which people repeatedly use such a product and update their rate of subsequent usage depending upon their satisfaction, we showed that overambitious seeding can result in people adopting in suboptimal contexts, having bad experiences, and abandoning the product before more favorable contexts for adoption arise. Here, we extend that earlier work by showing that the costs of overambitious seeding also appear in more traditional threshold models of collective behavior, once the possibility of permanent abandonment of the product is introduced. We further demonstrate that these costs can be mitigated by using conservative seeding approaches besides those that we explored in the earlier paper. Synthesizing these results with other recent work in this area, we identify general principles for when overambitious seeding can be of concern in the deployment of social products.

Human knowledge and innovation are recorded in two media: scholarly publication and patents. These records not only document a new scientific insight or new method developed, but they also carefully cite prior work upon which the innovation is built. We quantify the impact of information flow across fields using two large citation dataset: one spanning over a century of scholarly work in the natural sciences, social sciences and humanities, and second spanning a quarter century of United States patents. We find that a publication's citing across disciplines is tied to its subsequent impact. In the case of patents and natural science publications, those that are cited at least once are cited slightly more when they draw on research outside of their area. In contrast, in the social sciences, citing within one's own field tends to be positively correlated with impact.

Purpose Hospital-acquired infections with highly resistant organisms are an important problem among critically ill patients. Control of these organisms has largely focused within individual hospitals. We examine the extent to which transfers of critically ill patients could be a vector for the wide spread of highly resistant organisms, and compare the efficiency of different approaches to targeting infection control resources. Methods We analyzed the network of interhospital transfers of intensive care unit patients in 2005 US Medicare data and 2004–2006 Pennsylvania all-payer data. We simulated the spread of highly resistant hospital-acquired infections by randomly choosing a single hospital to develop a highly resistant organism and following the spread of infection or colonization throughout the network under varying strategies of infection control and varying levels of infectivity. Results Critical care transfers could spread a highly resistant organism between any two US hospitals in a median of 3 years. Hospitals varied substantially in their importance to limiting potential spread. Targeting resources to a small subset of hospitals on the basis of their position in the transfer network was 16 times more efficient than distributing infection control resources uniformly. Within any set of targeted hospitals, the best strategy for infection control heavily concentrated resources at a few particularly important hospitals, regardless of level of infectivity. Conclusions Critical care transfers provide a plausible vector for widespread dissemination of highly resistant hospital-acquired microorganisms. Infection control efforts can be made more efficient by selectively targeting hospitals most important for transmission.

To explore the effects of different incentives on crowdsourcing participation and submission quality, we conduct a randomized field experiment on Taskcn, a large Chinese crowdsourcing site using mechanisms with features of an all-pay auction. In our study, we systematically vary the size of the reward as well as the presence of a soft reserve, or early high-quality submission. We find that a higher reward induces significantly more submissions and submissions of higher quality. In comparison, we find that high-quality users are significantly less likely to enter tasks where a high-quality solution has already been submitted, resulting in lower overall quality in subsequent submissions in such soft reserve treatments.

The exponential growth of the World-Wide Web has transformed it into an ecology of knowledge in which highly diverse information is linked in an extremely complex and arbitrary manner. But even so, as we show here, there is order hidden in the web. We find that web pages are distributed among sites according to a universal power law: many sites have only a few pages, whereas very few sites have hundreds of thousands of pages. This universal distribution can be explained by using a simple stochastic dynamical growth model.

Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook's algorithmically ranked News Feed and further studied users' choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals' choices played a stronger role in limiting exposure to cross-cutting content.