Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities

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

doi:10.1016/j.focus.2022.100062

Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities

AJPM Focus. 2023 Mar;2(1):100062. doi: 10.1016/j.focus.2022.100062. Epub 2022 Dec 22.

Authors

Danielle Levanti¹, Rebecca N Monastero², Mohammadzaman Zamani³, Johannes C Eichstaedt⁴, Salvatore Giorgi⁵, H Andrew Schwartz³, Jaymie R Meliker⁶

Affiliations

¹ Undergraduate Studies, Cornell University, Ithaca, New York.
² Renaissance School of Medicine, Stony Brook University, Stony Brook, New York.
³ Department of Computer Science, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York.
⁴ Department of Psychology, School of Humanities and Sciences, Stanford University, Palo Alto, California.
⁵ Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania.
⁶ Program in Public Health, Department of Family, Population, & Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York.

Abstract

Introduction: Although surveys are a well-established instrument to capture the 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 because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data.

Methods: We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. 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 the 7 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 shows the 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.

Keywords: Social media; coronavirus; mental health; stay-at-home order.

Grants and funding

R01 MH125702/MH/NIMH NIH HHS/United States