2020
DOI: 10.1007/s11606-020-05988-8
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Tracking Mental Health and Symptom Mentions on Twitter During COVID-19

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Cited by 116 publications
(107 citation statements)
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“…Next, our temporal analyses pointed to a steady decline in people's expressed psychosocial concerns during the two month study period (Figure 1), which conforms with similar findings in Google search queries as stay-at-home orders and other COVID-19 related policy changes were implemented in the U.S. [104]. We note contemporary social computing research studying various aspects of the social media discourse related to COVID-19 [63,[105][106][107]. By providing complementary evidence to observations by Mackey et al [105] and Stokes et al [106] on expressed (mental health) concerns during the crisis, our work further underscores their findings using a comparable (control) dataset, reinforcing and providing empirical credibility to the impression that the COVID-19 pandemic has indeed caused or contributed directly to the mental health concerns that we describe.…”
Section: Comparison With Prior Worksupporting
confidence: 82%
“…Next, our temporal analyses pointed to a steady decline in people's expressed psychosocial concerns during the two month study period (Figure 1), which conforms with similar findings in Google search queries as stay-at-home orders and other COVID-19 related policy changes were implemented in the U.S. [104]. We note contemporary social computing research studying various aspects of the social media discourse related to COVID-19 [63,[105][106][107]. By providing complementary evidence to observations by Mackey et al [105] and Stokes et al [106] on expressed (mental health) concerns during the crisis, our work further underscores their findings using a comparable (control) dataset, reinforcing and providing empirical credibility to the impression that the COVID-19 pandemic has indeed caused or contributed directly to the mental health concerns that we describe.…”
Section: Comparison With Prior Worksupporting
confidence: 82%
“…During the early phases of the COVID-19 pandemic, the Penn Medicine Center for Digital Health and the World Well-Being Project launched a public-facing platform that uses machine learning approaches to synthesize data from Twitter about COVID-19 in real time. 5 Based on data from this platform and others and to enhance public awareness, the Washington State Department of Health posts weekly online behavioral health situation reports containing regional estimates of sentiment, loneliness, and anxiety. 6 This approach could be further harnessed to support situational awareness and enable more directed health messaging to address well-being and population-level mental health needs in response to COVID-19.…”
Section: Surveillance Of Digital Data To Inform Public Health Messagingmentioning
confidence: 99%
“…An analysis of sentiments expressed on social media after the national declaration of emergency compared to the same time period in 2019 demonstrates that the pandemic is having an unprecedented impact on well-being with stress, anxiety, and loneliness becoming markedly higher among the U.S. population (Guntuku et al, 2020). In the college population, the COVID-19 pandemic has had significant effects on students' anxiety and depression (Cao et al, 2020;Chirikov et al, 2020;Liu, Liu, & Zhong, 2020).…”
Section: Covid-19 In College Populationsmentioning
confidence: 99%