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INTRODUCTION : While surveys are a well-established instrument to capture population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders due to COVID-19, and anxiety and depression in seven major US cities utilizing Twitter data.

METHODS : We collected 18 million Tweets from January to September 2019 (baseline), and 2020 from seven US cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine-learning-based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google trends data using search query frequencies. A qualitative evaluation of trends is presented.

RESULTS : Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all seven locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer, and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual States. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results.

CONCLUSIONS : Our study demonstrates feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.

Levanti Danielle, Monastero Rebecca N, Zamani Mohammadzaman, Eichstaedt Johannes C, Giorgi Salvatore, Schwartz H Andrew, Meliker Jaymie R


coronavirus, mental health, social media, stay-at-home order