cUBelab

The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.

The John H. Chafee Foster Care Program for Successful Transition to Adulthood (CFCIP) allocates funding to provide services to youth who are likely to age out of foster care. These services, covering everything from mentoring to financial aid, are expected to be distributed in ways that prepare youth for life after care. One natural question to ask is, which youth receive Chafee services? The present work makes use of the National Youth in Transition Database (NYTD), a large-scale administrative dataset that tracks services allocated to youth that use CFCIP funds to answer this question. Specifically, we conduct a forensic social science analysis of the NYTD data. To do so, we first use computational methods to help us uncover the factors that best predict which youth will receive services associated with service receipt. We find that the majority of variables in the Adoption and Foster Care Analysis and Reporting System (AFCARS) and NYTD have limited or no utility in predicting Chafee service receipt, and that a subset of three variables—youth age, youth time in care, and the state in which a youth is in care—explain almost all variability in service receipt. We conclude with a discussion of the implications of these and other findings on future research on Chafee service allocation, and the utility of predictive modeling in child welfare, with a particular focus on the utility of the NYTD in this context.

New data and technologies, in particular machine learning, may make it possible to forecast neighbourhood change. Doing so may help, for example, to prevent the negative impacts of gentrification on marginalised communities. However, predictive models of neighbourhood change face four challenges: accuracy (are they right?), granularity (are they right at spatial or temporal scales that actually matter for a policy response?), bias (are they equitable?) and expert validity (do models and their predictions make sense to domain experts?). The present work provides a framework to evaluate the performance of predictive models of neighbourhood change along these four dimensions. We illustrate the application of our evaluation framework via a case study of Buffalo, NY, where we consider the following prediction task: given historical data, can we predict the percentage of residential buildings that will be sold or foreclosed on in a given area over a fixed amount of time into the future?

Academic fields exhibit substantial levels of gender segregation. Here, we investigated differences in field-specific ability beliefs (FABs) as an explanation for this phenomenon. FABs may contribute to gender segregation to the extent that they portray success as depending on “brilliance” (i.e., exceptional intellectual ability), which is a trait culturally associated with men more than women. Although prior work has documented a relation between academic fields’ FABs and their gender composition, it is still unclear what the underlying dynamics are that give rise to gender imbalances across academia as a function of FABs. To provide insight into this issue, we custom-built a new data set by combining information from the author-tracking service Open Researcher and Contributor ID (ORCID) with information from a survey of U.S. academics across 30 fields. Using this expansive longitudinal data set (Ns = 86,879–364,355), we found that women were underrepresented among those who enter fields with brilliance-oriented FABs and overrepresented among those who exit these fields. We also found that FABs’ association with women’s transitions across academic fields was substantially stronger than their association with men’s transitions. With respect to mechanisms, FABs’ association with gender segregation was partially explained by the fact that women encounter more prejudice in fields with brilliance-oriented FABs. With its focus on the dynamic patterns shaping segregation and its broad scope in terms of geography, career stage, and historical time, this research makes an important contribution toward understanding the factors driving gender segregation in academia.

Therapeutic foster care agencies provide temporary placements and a range of services to at-risk youth to help ensure their safety, permanency, and wellbeing. The practitioners that plan such care operate under heavy caseloads, limited resources, and high stakes. There is significant interest in supporting these practitioners with various technological interventions, but their work and the context around it is still poorly understood. This study aims to better understand the current assessment and treatment planning work in therapeutic foster care. We used the abstraction hierarchy modeling approach to outline the purposes, values, constraints, processes, and tools that define the workplace ecology encountered by care coordinators and clinicians from therapeutic foster care programs at Hillside, a collaborating human service organization. The resulting abstraction hierarchy was closely examined to identify areas for interventions and design implications.

Two-sided matching markets have long existed to pair agents in the absence of regulated exchanges. A common example is school choice, where a matching mechanism uses student and school preferences to assign students to schools. In such settings, forming preferences is both difficult and critical. Prior work has suggested various prediction mechanisms that help agents make decisions about their preferences. Although often deployed together, these matching and prediction mechanisms are almost always analyzed separately. The present work shows that at the intersection of the two lies a previously unexplored type of strategic behavior: agents returning to the market (e.g., schools) can attack future predictions by interacting short-term non-optimally with their matches. Here, we first introduce this type of strategic behavior, which we call an adversarial interaction attack. Next, we construct a formal economic model that captures the feedback loop between prediction mechanisms designed to assist agents and the matching mechanism used to pair them. Finally, in a simplified setting, we prove that returning agents can benefit from using adversarial interaction attacks and gain progressively more as the trust in and accuracy of predictions increases. We also show that this attack increases inequality in the student population.