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


ABSTRACT:

Background

During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.

Methods

COVID-19-related tweets from March 5th, 2020, to January 31st, 2021, were collected through Twitter streaming API using keywords (i.e., "corona," "covid19," and "covid"). By further filtering using keywords (i.e., "depress," "failure," and "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.

Results

We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home," "death poll," and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.

Conclusions

The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.

SUBMITTER: Zhang S 

PROVIDER: S-EPMC9629680 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Zhang Senqi S   Sun Li L   Zhang Daiwei D   Li Pin P   Liu Yue Y   Anand Ajay A   Xie Zidian Z   Li Dongmei D  

Health data science 20220217


<h4>Background</h4>During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.<h4>Methods</h4>COVID-19-related tweets from March 5<sup>th</sup>, 2020, to January 31<sup>st</sup>, 2021, were collected through Twitter streaming API using keywords (i.e.  ...[more]

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