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Abstract
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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 expressions in the pandemic context.
METHODS: We obtain ∼60M Twitter streaming posts originating from the U.S. from 24 March-24 May 2020 and compare these with ∼40M posts from a comparable period in 2019 to 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 of 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 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 very specific to and 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 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.
CLINICALTRIAL:
Language: en |