Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed-Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside "social bubbles." Our results could lead to a deeper understanding of how technology biases our exposure to new information.

Federal funding is associated with the quality of science and researchers' professional advancement. 1 Female junior faculty received less university start-up support than males in one study, 2 a factor associated with early-career attrition rates. 3 We investigated another potential association: the size of National Institutes of Health (NIH) grant awards to first-time awardees.Methods | Using the public NIH Principal Investigators (PI) database, we analyzed grant amounts to first-time female and male grant awardees from 2006 to 2017. A PI's sex was determined algorithmically from first names. First-time PIs had no prior NIH awards as far back as 1985.To examine factors related to funding, we first compared the median number of articles published per year, the median number of citations per article, and the number of areas of research expertise in published articles for first-time female and male PIs prior to their first NIH grant, using Microsoft Academic Graph (MAG). Areas of research expertise were estimated from the articles' research topic as reported in MAG. Only articles with the PI as the last author were counted. 4 To further control for confounding, we examined awardees of the top 10 most highly funded grants awarded to individual PIs only, which represents $14 billion in funding or 58% of all NIH funds awarded to 19 559 first-time PIs. Also, we investigated awardees at the same 14 Big Ten and 8 Ivy League universities ($1.8 billion in funding or 7.5% of NIH funds awarded to 8039 first-time PIs), as well as the top 50 NIH most highly funded institutions ($9 billion in funding or 38% of funding awarded to 20 335 first-time PIs). The 2-sided Mann-Whitney test of medians (threshold P < .05) and Python software (version 2.7.12) were used in the analyses.

Social media are massive marketplaces where ideas and news compete for our attention [1]. Previous studies have shown that quality is not a necessary condition for online virality [2] and that knowledge about peer choices can distort the relationship between quality and popularity [3]. However, these results do not explain the viral spread of low-quality information, such as the digital misinformation that threatens our democracy [4]. We investigate quality discrimination in a stylized model of online social network, where individual agents prefer quality information, but have behavioral limitations in managing a heavy flow of information. We measure the relationship between the quality of an idea and its likelihood to become prevalent at the system level. We find that both information overload and limited attention contribute to a degradation in the market's discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model predicts that high-quality information has little advantage over low-quality information. * These two authors contributed equally to this work

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