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The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively "unimportant" story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others' political identities. Observational follow network data collected on Twitter support this prediction: We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be "unimportant" news at the expense of missing out on subjectively "important" news far more frequently. This suggests that "echo chambers"—to the extent that they exist—may not echo so much as silence.

Efforts aimed at broadening participation in science, technology, engineering, and mathematics (STEM) require a holistic presentation of the state of racial and gender participation. Statistics currently used to describe participation often include raw counts of degrees and the percentages of demographic groups receiving STEM degrees. While these data provide insights into demographic trends, they do not present the complete picture because these "traditional" statistics do not capture how well a field of study reflects—or is proportionally similar to—a larger body, such as the college population. If the goal of broadening participation in STEM education is to ensure that all racial and gender groups are proportionally represented, analysts require direct measures of representation. In this article, we present a novel metric that assesses the degree to which groups are overrepresented or underrepresented in a given field. This metric calculates field-specific representation by comparing the proportion of degrees awarded to members of a demographic group in a specific field of study with the proportion of all degrees awarded to that group. Using data from the National Science Foundation and the Department of Education, we demonstrate the efficacy of this representation metric and show that it provides new insights into STEM participation levels for women and other groups considered to be underrepresented. While traditional measurements show the increasing number of degrees awarded to and the increasing share of underrepresented minority students in STEM, our metric revealed that underrepresented minorities remain underrepresented in STEM fields, especially in engineering and the natural sciences.