The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

Zhang, Senqi; Sun, Li; Zhang, Daiwei; Li, Pin; Liu, Yue; Anand, Ajay; Xie, Zidian; Li, Dongmei

doi:10.34133/2022/9758408

Cited by 13 publications

(8 citation statements)

References 28 publications

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“…Consistent with research on similar topics, we found that COVID-19 has severely impacted public mental health and has dynamic influences on public mental health [30,42]. In addition, we found that the proportion of anxiety-related tweets increased to a substantial peak in March 2020 and remained low but stable for several months.…”

Section: Comparison To Prior Worksupporting

confidence: 86%

“…Meanwhile, Berry et al [29] pointed out through a study with both quantitative and qualitative approaches that people are willing to discuss mental health problems on Twitter for varied reasons, including the sense of community and Twitter being a safe space for expression, coping, empowerment, etc. However, existing literature on public mental health during the pandemic using Twitter data [30][31][32][33] either has short study periods and small sample sizes or does not focus on subtypes of mental health problems and subgroup prevalence. More granular study designs and more comprehensive data are needed for such studies.…”

Section: Introductionmentioning

confidence: 99%

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Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study

Li¹,

Hua²,

Liao³

et al. 2022

J Med Internet Res

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Background The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Understanding and tracking changes in public mental status can facilitate optimizing public mental health intervention and control strategies. Objective This study aimed to build a social media–based pipeline that tracks public mental changes and use it to understand public mental health status regarding the pandemic. Methods This study used COVID-19–related tweets posted from February 2020 to April 2022. The tweets were downloaded using unique identifiers through the Twitter application programming interface. We created a lexicon of 4 mental health problems (depression, anxiety, insomnia, and addiction) to identify mental health–related tweets and developed a dictionary for identifying health care workers. We analyzed temporal and geographic distributions of public mental health status during the pandemic and further compared distributions among health care workers versus the general public, supplemented by topic modeling on their underlying foci. Finally, we used interrupted time series analysis to examine the statewide impact of a lockdown policy on public mental health in 12 states. Results We extracted 4,213,005 tweets related to mental health and COVID-19 from 2,316,817 users. Of these tweets, 2,161,357 (51.3%) were related to “depression,” whereas 1,923,635 (45.66%), 225,205 (5.35%), and 150,006 (3.56%) were related to “anxiety,” “insomnia,” and “addiction,” respectively. Compared to the general public, health care workers had higher risks of all 4 types of problems (all P<.001), and they were more concerned about clinical topics than everyday issues (eg, “students’ pressure,” “panic buying,” and “fuel problems”) than the general public. Finally, the lockdown policy had significant associations with public mental health in 4 out of the 12 states we studied, among which Pennsylvania showed a positive association, whereas Michigan, North Carolina, and Ohio showed the opposite (all P<.05). Conclusions The impact of COVID-19 and the corresponding control measures on the public’s mental status is dynamic and shows variability among different cohorts regarding disease types, occupations, and regional groups. Health agencies and policy makers should primarily focus on depression (reported by 51.3% of the tweets) and insomnia (which has had an ever-increasing trend since the beginning of the pandemic), especially among health care workers. Our pipeline timely tracks and analyzes public mental health changes, especially when primary studies and large-scale surveys are difficult to conduct.

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Section: Comparison To Prior Worksupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study

Li¹,

Hua²,

Liao³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Digital modernity also received the mother of all exogenous pushes when the COVID-19 pandemic made offline interaction riskier, and often illegal, accelerating the migration online (indeed, the development of e-commerce in Eastern Asia was kick-started in the first place by the smaller-scale disruption of the SARS epidemic of 2002-04). And with the quantity of data created and the increased amount of processing required, the cloud, or more accurately clouds, are only going to grow in the medium term -especially given the successes, failures and potential of the ambient data infrastructure in combating COVID-19 itself, for example in data science (Dayan et al, 2021;Podder et al, 2021), epidemiology (Khakharia et al, 2021;Rahimi et al, 2021), tracking and tracing (Abbas and Michael, 2020), and monitoring public attitudes (Perrotta et al, 2021;Zhang et al, 2022). Of course, it was also used for misinformation (Brennen et al, 2020;Shahi et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Digital Modernity

O’Hara

2022

FNT in Web Science

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Modernity" is a social, cultural or historical descriptor for a certain type of society or set of social arrangements. This monograph reviews narratives of digital modernity, without endorsing them; as narratives, they selectively discuss aspects of our sociotechnical context, descriptively, teleologically or normatively. Digital modernity narratives focus on the possibilities of the data gathered by an ambient data infrastructure, enabled by ubiquitous devices such as the smartphone, and activities such as social networking and e-commerce. Some emphasise continuities with 20th century modernity narratives, while others emphasise discontinuity, such as theories of the singularity. Digital modernity is characterised by: a subjunctive outlook where people's choices can be anticipated and improved upon; the valorisation of disruptive innovation on demand; and control provided by data analysis within a virtual realm (cyberspace or the metaverse) which can be extended and applied to the physical world (in such applications as the quantified self and the smart city). The synergies and tensions between these three aspects are explored, as are the opportunities for and dilemmas posed by misinformation. Five principles emerge from the study of relevant texts and business models: (1) the quantity of data being produced in the world has enabled, and been enabled by, technological, social, economic and

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“…It collects data through the network to observe human psychology and behavior (36,37). At present, researchers have used this method to measure the public's reaction to government policies such as mental health and social prejudice (38,39). In the aspect of emotional attitude research, we also began to use this method to carry out relevant research (40).…”

Section: Introductionmentioning

confidence: 99%

A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government

Zhang

Niu

Yang

et al. 2023

Front. Public Health

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IntroductionSince the outbreak of the COVID-19 pandemic, the Chinese government has implemented a series of strict prevention and control policies to prevent the spread of the virus. Recently, the Chinese government suddenly changed its approach and lifted all prevention and control measures. This sudden change in policy is expected to lead to a widespread outbreak of COVID-19 in China, and the public and local governments are not adequately prepared for the unknown impact on society. The change in the “emergency” prevention and control policy provides a unique research perspective for this study.MethodsThe purpose of this study is to analyze the public's attitudes and emotional responses to COVID-19 under the sudden opening policy, identify the key factors that contribute to these attitudes and emotions, and propose solutions. In response to this sudden situation, we conducted data mining on topics and discussions related to the opening of the epidemic on Sina Weibo, collecting 125,686 interactive comments. We used artificial intelligence technology to analyze the attitudes and emotions reflected in each data point, identify the key factors that contribute to these attitudes and emotions, explore the underlying reasons, and find corresponding solutions.ResultsThe results of the study show that in the face of the sudden release of the epidemic, the public mostly exhibited negative emotions and behaviors, with many people experiencing anxiety and panic. However, the gradual resumption of daily life and work has also led some people to exhibit positive attitudes.ConclusionThe significance of this study is to help the government and institutions understand the impact of policy implementation on users, and to enable them to adjust policies in a timely manner to respond to potential social risks. The government, emergency departments, and the public can all prepare for similar situations based on the conclusions of this study.

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The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

Cited by 13 publications

References 28 publications

Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study

Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study

Digital Modernity

A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government

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