Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people’s lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media—and the near-ubiquity of mobile devices used to access social media networks—offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have “opted in” for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention—we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?

Tragically, an estimated 42,000 Americans died by suicide in 2015, each one deeply affecting friends and family. Very little data and information is available about people who attempt to take their life, and thus scientific exploration has been hampered. We examine data from Twitter users who have attempted to take their life and provide an exploratory analysis of patterns in language and emotions around their attempt. We also show differences between those who have attempted to take their life and matched controls. We find quantifiable signals of suicide attempts in the language of social media data and estimate performance of a simple machine learning classifier with these signals as a non-invasive analysis in a screening process.

We propose a new end-to-end neural acoustic model for automatic speech recognition. The model is composed of multiple blocks with residual connections between them. Each block consists of one or more modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers. It is trained with CTC loss. The proposed network achieves near state-of-the-art accuracy on LibriSpeech and Wall Street Journal, while having fewer parameters than all competing models. We also demonstrate that this model can be effectively fine-tuned on new datasets.

This research explores the macro-level influences of religion on the marketplace by showing how religion influences beliefs of dominion and stewardship, which subsequently influence marketplace attitudes and sustainable behavior. A survey of 1,101 adults was conducted, with results showing religious individuals express greater beliefs of dominion while non-religious individuals express greater beliefs of stewardship. Stewardship beliefs in turn positively influence one’s tendency to engage in sustainable behavior, while dominion does not. These beliefs also mediate the relationship between religiosity and behavior, though the effects of dominion are negative and weaker than those of stewardship. We also provide insight into whom consumers hold responsible for solving sustainability issues, with the non-religious placing responsibility on consumers and the religious placing responsibility on producers. We build off value-belief-norm and attribution theories to discuss how our findings contribute to sustainability in marketing systems and provide greater understanding of the intersection between religion and sustainability.