Predicting public mental health needs in a crisis using situational indicators and social media emotions: A Singapore big data study (preprint)

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.

How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India (preprint)

The COVID-19 pandemic has claimed millions of lives worldwide and elicited heightened emotions. This study examines the expression of various emotions pertaining to COVID-19 in the United States and India as manifested in over 54 million tweets, covering the fifteen-month period from February 2020 through April 2021, a period which includes the beginnings of the huge and disastrous increase in COVID-19 cases that started to ravage India in March 2021. Employing pre-trained emotion analysis and topic modeling algorithms, four distinct types of emotions (fear, anger, happiness, and sadness) and their time- and location-associated variations were examined. Results revealed significant country differences and temporal changes in the relative proportions of fear, anger, and happiness, with fear declining and anger and happiness fluctuating in 2020 until new situations over the first four months of 2021 reversed the trends. Detected differences are discussed briefly in terms of the latent topics revealed and through the lens of appraisal theories of emotions, and the implications of the findings are discussed.