Social Media Reveals Psychosocial Effects of the COVID-19 Pandemic
Saha, K.; Torous, J.; Caine, E. D.; De Choudhury, M.
Show abstract
BackgroundThe novel coronavirus disease 2019 (COVID-19) pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multi-faceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a "mental health tsunami," the psychological effects of the COVID-19 crisis remains unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. ObjectiveOur work aims to provide insights regarding peoples psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support-seeking expressions in the pandemic context. MethodsWe obtain ~60M Twitter streaming posts originating from the U.S. from March, 24 - May, 25, 2020, and compare these with ~40M posts from a comparable period in 2019 to causally attribute the effect of COVID-19 on peoples social media self-disclosure. Using these datasets, we study peoples self-disclosure on social media in terms of symptomatic mental health concerns and expressions seeking support. We employ transfer learning classifiers that identify the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examine the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 datasets. ResultsWe find that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis - mental health symptomatic expressions have increased by ~14%, and support seeking expressions have increased by ~5%, both thematically related to COVID-19. We also observe a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlight that people express concerns that are contextually related to the COVID-19 crisis. ConclusionsWe studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that peoples mental health symptomatic and support-seeking expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their "new normal". Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, healthcare and precautionary measures, and pandemic-related awareness. This work shows the potential to provide insights to mental healthcare and stakeholders and policymakers in planning and implementing measures to mitigate mental health risks amidst the health crisis.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Tracking private WhatsApp discourse about COVID-19: A longitudinal infodemiology study in Singapore 95%
- Developing an automatic system for classifying chatter about health services from Twitter: A case study for Medicaid 93%
- Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter using machine learning 93%
Similar papers in this journal
- Understanding mental health trends during COVID-19 pandemic in the United States using network analysis 94%
- The Pandemic Journaling Project: A new dataset of first-person accounts of the COVID-19 pandemic 94%
- Who is (Not) Complying with the Social Distancing Directive and Why? Testing a General Framework of Compliance with Multiple Measures of Social Distancing 92%
Similar papers in this journal
- Passive sensing data predicts stress in university students: A supervised machine learning method for digital phenotyping 93%
- Use of the Internet and digital devices among people with severe mental ill health during the COVID-19 pandemic restrictions 92%
- Computational Psychiatry Research Map (CPSYMAP): a New Database for Visualizing Research Papers 91%
Similar papers in this journal
- Evaluating and mitigating unfairness in multimodal remote mental health assessments 94%
- Use of large language models as a scalable approach to understanding public health discourse 93%
- Defining Destigmatizing Design Guidelines for Use in Sexual Health-Related Digital Technologies: A Delphi Study 92%
Similar papers in this journal
- Toward Using Twitter Data to Monitor Covid-19 Vaccine Safety in Pregnancy 93%
- Development and use analysis of ‘gestioemocional.cat’, a web app for promoting emotional self-care and access to professional mental health resources during the covid-19 pandemic 92%
- The potential for digital patient symptom recording through symptom assessment applications to optimize patient flow and reduce waiting times in Urgent Care Centers: a simulation study 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.