UC Riverside

Analyses of Google internet searches, reveal deep and otherwise elusive sentiments that are all too frequently unobserved by traditional research methods. Internet users often search topics they would be unwilling to ask their closest and most intimate confidants about. Thoughts about suicide, depression and the means one might use to take one’s own life are prominent examples – people may keep these thoughts very private but reveal them to google. Spring boarding from this central insight this paper seeks to determine whether suicide rates are higher in places where google searches about depression and suicide are common. Building on prior research, this project will assess the extent to which Google searches predict the 50-state suicide rate above and beyond traditional approaches. Using factor analysis three clusters of search terms were identified: suicide, depression, and gun-related. Suicidal and depressive search terms are found to be significantly associated with the suicide rate over the 11-year period (2006- 2016) even after controlling for divorce, unemployment, mental state, binge drinking, gun ownership, and yearly effects. These findings suggest that internet search data represent a, practical, and cost-effective method for studying suicide and other large-scale sociological phenomena. As fields such as public health, epidemiology, and psychiatry effectively incorporate internet and big data-driven methodologies, it is imperative that sociologists carefully consider its capabilities given its increasing utilization and potential.