Social Media Reveals Psychosocial Effects of the COVID-19 Pandemic

Abstract

Background: The 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. Objective: Our work aims to provide insights regarding people's 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. Methods: We obtain 60M Twitter streaming posts originating from the U.S. from March, 24 to May, 25, 2020, and compare these with 40M posts from a comparable period in 2019 to causally attribute the effect of COVID-19 on people's social media self-disclosure. Using these datasets, we study people's 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. Results: We 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. Conclusions: We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's 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.

COVID-19-Pandemie trifft auf Psyche der Bevölkerung: Gibt es einen Tsunami psychischer Störungen?

“…Monitoringansätze wie die COSMO-Studie, die nach Angst direkt fragt und wöchentliche Ergebnisse vorlegt, kommt zu einem ähnlichen Bild [22]. Neuere Datenanalysen, die auf Social Media und Suchmaschinen-Abfragen basieren, bestätigen diese rückläufigen Trends ebenfalls [23,24]. Als weniger wahrscheinliche, aber alternative Erklärung kommen auch Copingund Adaptationseffekte infrage.…”

Evaluation of County-Level Heterogeneity in Excess Mortality in Colorado from March to September 2020

“…Furthermore, we seek to better understand the mix of significant contributing factors that led to deaths from SARS-CoV-2 as the direct cause, and plan to investigate the distribution of underlying causes of death among patients with COVID-19 listed as secondary on their certificates. More broadly, downstream repercussions of NPIs such as stayat-home orders on routine care, chronic diseases, and socioeconomic factors -tied to mental health [65,66] and well-being [67,68,69] -are evident and deserve to be carefully studied to better inform potential courses of action.…”

Prediction and detection of emotional tone in online social media mental disorder groups using regression and recurrent neural networks

“…The authors work in [16] used transfer learning classifiers that recognize the social media language indicative of mental health outcomes for studying the changes in psycho-social expressions over language and time during Covid-19. The proposed model can study not only the linguistic changes but also the temporal changes in combination with support expressions.…”

“…4 [16], (2020) Using transfer learning classifiers that identify the social media language indicative of mental health outcomes for examining the changes in psychosocial expressions over time and language during Covid-19.…”