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. 2020 Jul 10;17(14):4988.
doi: 10.3390/ijerph17144988.

Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining

Diya Li  1 , Harshita Chaudhary  2 , Zhe Zhang  1
Affiliations
Free PMC article

Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining

Diya Li et al. Int J Environ Res Public Health. .
Free PMC article

Abstract

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people's stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people's risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.

Keywords: Basilisk algorithm; COVID-19 pandemic; Correlation Explanation (CorEx); Patient Health Questionnaire (PHQ); mental health; social media data mining.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Fuzzy membership functions of uncertainty evaluation of assigned PHQ category.
Figure 2
Figure 2
Process undertaken to generate spatiotemporal stress symptom maps and topics.
Figure 3
Figure 3
Spatiotemporal pattern with fuzzy accuracy assessment for stress symptom analysis result generated by CorExQ9. (a) from 01.26.2020 to 02.09.2020; (b) from 02.09.2020 to 02.23.2020; (c) from 02.23.2020 to 03.08.2020; (d) from 03.08.2020 to 03.22.2020; (e) from 03.22.2020 to 04.05.2020; (f) from 04.05.2020 to 04.19.2020; (g) from 04.19.2020 to 05.03.2020; (h) The legend of for (a)–(g).
Figure 3
Figure 3
Spatiotemporal pattern with fuzzy accuracy assessment for stress symptom analysis result generated by CorExQ9. (a) from 01.26.2020 to 02.09.2020; (b) from 02.09.2020 to 02.23.2020; (c) from 02.23.2020 to 03.08.2020; (d) from 03.08.2020 to 03.22.2020; (e) from 03.22.2020 to 04.05.2020; (f) from 04.05.2020 to 04.19.2020; (g) from 04.19.2020 to 05.03.2020; (h) The legend of for (a)–(g).
Figure 4
Figure 4
The number of daily confirmed new cases in the United State (five-day moving average) [63,64].
See this image and copyright information in PMC

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