The need to protect privacy poses unique challenges to behavioral research. For instance, researchers often can not use examples drawn directly from such data to explain or illustrate key findings. In this research, we use data-driven models to synthesize realistic-looking data, focusing on discourse produced by social-media participants announcing life-changing events. We comparatively explore the performance of distinct techniques for generating synthetic linguistic data across different linguistic units and topics. Our approach offers utility not only for reporting on qualitative behavioral research on such data, where directly quoting a participant's content can unintentionally reveal sensitive information about the participant, but also for clinical computational system developers, for whom access to realistic synthetic data may be sufficient for the software development process. Accordingly, the work also has implications for computational linguistics at large.

This study indicates that death in downhill skiing is disproportionately dominated by males (83% males versus only 63% males in the population at risk). Fatally injured skiers are older than the general skiing public, who in turn are older than the typically injured skier. The typical fatal accident scenario involves an experienced male running into a tree off the edge of an intermediate skill level slope at a high rate of speed, resulting in massive head or neck injury, or both, leading to death. The demographic (age and gender) pattern of fatally injured skiers is very similar to automobile and occupational trauma related death. The rate of fatal injury to downhill skiers is 0.55 deaths per million skier visits, and has not significantly changed over the past ten ski seasons. When the death rate is expressed in terms of fatalities per million hours of exposure, skiing is about seven times safer than travel by automobile or commercial aircraft.

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