ABSTRACT
Mental health issues and needs have increased substantially since the onset of the COVID-19 pandemic. However, health policy and decision-makers do not have adequate data and tools to predict population-level mental health demand, especially amid a crisis. This study investigates whether situational indicators and social media emotions can be effectively used to predict public mental health needs. We collected time-series data from multiple sources in Singapore between 1 July 2020 and 31 December 2021, including daily-level records of situation indicators, emotions expressed on social media, and mental health needs measured by the number of public visits to the emergency room of the country's largest psychiatric hospital, and use of government-initiated online mental health self-help portal. Compared to mental health needs data alone, social media emotions were found to have significant Granger-causality effects with as early as four to five days lag length. Each resulted in a statistically significant enhancement in predicting the public's visits to the emergency room and the online self-help portal (e.g., Facebook Anger Count on emergency room visits, χ2 = 13·7, P = ·0085**). In contrast, situational indicators such as daily new cases had Granger-causality effects (χ2 = 10·3, P = ·016*) with a moderate lag length of three days. The findings indicate that emotions algorithmically extracted from social media platforms can provide new indicators for tracking and forecasting population-level mental health states and needs.