Online publishing, social networks, and web search have dramatically lowered the costs to produce, distribute, and discover news articles. Some scholars argue that such technological changes increase exposure to diverse perspectives, while others worry they increase ideological segregation. We address the issue by examining web browsing histories for 50,000 U.S.-located users who regularly read online news. We find that social networks and search engines increase the mean ideological distance between individuals. However, somewhat counterintuitively, we also find these same channels increase an individual's exposure to material from his or her less preferred side of the political spectrum. Finally, we show that the vast majority of online news consumption is accounted for by individuals simply visiting the home pages of their favorite, typically mainstream, news outlets, tempering the consequences-both positive and negative-of recent technological changes. We thus uncover evidence for both sides of the debate, while also finding that the magnitude of the e↵ects are relatively modest.

Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are substantially more likely than white defendants to be incorrectly classi ed as high risk. To mitigate such disparities, several techniques have recently been proposed to achieve algorithmic fairness. Here we reformulate algorithmic fairness as constrained optimization: the objective is to maximize public safety while satisfying formal fairness constraints designed to reduce racial disparities. We show that for several past de nitions of fairness, the optimal algorithms that result require detaining defendants above race-speci c risk thresholds. We further show that the optimal unconstrained algorithm requires applying a single, uniform threshold to all defendants. e unconstrained algorithm thus maximizes public safety while also satisfying one important understanding of equality: that all individuals are held to the same standard, irrespective of race. Because the optimal constrained and unconstrained algorithms generally di er, there is tension between improving public safety and satisfying prevailing notions of algorithmic fairness. By examining data from Broward County, Florida, we show that this trade-o can be large in practice. We focus on algorithms for pretrial release decisions, but the principles we discuss apply to other domains, and also to human decision makers carrying out structured decision rules. ACM Reference format:1 We consider racial disparities because they have been at the center of many recent debates in criminal justice, but the same logic applies across a range of possible a ributes, including gender. arXiv:1701.08230v4 [cs.CY]

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.

Respondent-driven sampling (RDS) is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries and by leading public health organizations, including the Centers for Disease Control and Prevention in the United States. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. Here we investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow. Moreover, because we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly, it is unlikely that RDS performs any better in practice than in our simulations. Notably, the poor performance of RDS is driven not by the bias but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied.disease surveillance | snowball sampling | social networks T he development and evaluation of public health policies often require detailed information about so-called hard-to-reach or hidden populations. For example, HIV researchers are especially interested in monitoring risk behavior and disease prevalence among injection drug users, men who have sex with men, and commercial sex workers-the groups at highest risk for HIV in most countries. Unfortunately, however, these high-risk groups are not easily studied with standard sampling methods, including institutional sampling, targeted sampling, and time-location sampling (1).Respondent-driven sampling (RDS) (2-4) facilitates examination of such hidden populations via a chain-referral procedure in which participants recruit one another, akin to snowball sampling. RDS is now widely used in the public health community and has been recently applied in more than 120 studies in more than 20 countries, involving a total of more than 32,000 participants (5). In particular, in helping to track the HIV epidemic, RDS is used by the Centers for Disease Control and Prevention (CDC) (6, 7) and by the United States President's Emergency Plan for AIDS Relief.RDS is a method both for data collection and for statistical inference. To generate an RDS sample, one begins by selecting a small number of initial participants ("seeds") from the target population who are asked-and typically provided financial incentive-to recruit their contacts in the population (2). The sampling proceeds with current sample members recruiting the next wave of sample members, continuing until the desired sample size is reached. Participants are usually all...