Digital Traces of Sexualities: Understanding the Salience of Sexual Identity through Disclosure on Social Media.

We analyze the expression of sexualities in the contemporary United States using data about disclosure on social media. Through the Facebook advertising platform, we collect aggregate counts encompassing 200 million Facebook users, 28% of whom disclose sexuality-related information. Stratifying by age, gender, and relationship status, we show how these attributes structure the propensity to disclose different sexual identities. We find a large generational difference; younger social media users share their sexualities at high rates, while for older cohorts marital status substitutes for sexual identity. Consistent with gendered expectations, women more often express a bisexual interest in men and women; men are more explicit about their heterosexuality. We interpret these variations in sexuality disclosure on social media to reflect the salience of sexual identity, intersected at times with availability. Our study contributes to the sociology of sexuality with a quantitative analysis, using novel digital data, of how sexuality is signaled socially.

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Humans in the Loop: Incorporating Expert and Crowd-Sourced Knowledge for Predictions Using Survey Data.

Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a "human-in-the-loop" approach in the Fragile Families Challenge. The authors use surveys to elicit knowledge from experts and laypeople about the importance of different variables to different outcomes. This strategy offers the option to subset the data before prediction or to incorporate human knowledge as scores in prediction models, or both together. The authors find that human intervention is not obviously helpful. Human-informed subsetting reduces predictive performance, and considered alone, approaches incorporating scores perform marginally worse than approaches that do not. However, incorporating human knowledge may still improve predictive performance, and future research should consider new ways of doing so.