What can thousands of personal stories tell us about the reasons people keep going during their darkest moments?
University of Maryland computational linguist Philip Resnik and clinical psychologist Rebecca Resnik recently joined CBS News correspondent Natalie Brand to discuss new research examining that question.
Philip Resnik is the lead author of the forthcoming Scientific Reports paper “16,648 Reasons to Live Instead of Dying by Suicide: Insights from a Computer-Assisted Content Analysis.” A professor of linguistics with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS), he leads research combining machine learning, computational linguistics and human expertise to study complex questions in mental health.
The research team used machine learning to sift through more than 16,000 firsthand accounts and identify recurring patterns, which experts then interpreted and organized. The analysis highlighted themes including concern for loved ones and pets, everyday pleasures such as music and television, hope and a sense of meaning, and even emotions such as fear, anger and spite.
For Rebecca Resnik, a clinical psychologist who collaborated with her husband on the project, the findings also have potential relevance to clinical care. Paying attention to seemingly small sources of connection and anticipation can provide important insight into what matters to an individual.
The CBS interview offers a closer look at the researchers’ work and at how new computational approaches are helping expand the study of mental health.