ArticleInternational journal of environmental research and public health2020
Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review.Public health · 2021Pooled it
- Thematic Mapping and Evolution of Social Media Mining in Health Research: Hybrid Bibliometric Synthesis.Journal of medical Internet research · 2026Review
- A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data.Scientific reports · 2024Article
- Article
- A systematic review on automated clinical depression diagnosis.Npj mental health research · 2023Review
- Social media analysis reveals environmental injustices in Philadelphia urban parks.Scientific reports · 2023Article
- Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter using deep learning.Online social networks and media · 2023Article
- Epicosm-a framework for linking online social media in epidemiological cohorts.International journal of epidemiology · 2023Article
- MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligence.PloS one · 2023Article
- A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.Database : the journal of biological databases and curation · 2022Article
- Mining Social Media Data to Predict COVID-19 Case Counts.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Determining the efficiency of data analysis systems in predicting COVID-19 infected cases.Journal of family medicine and primary care · 2022Article
- Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review.JMIR mental health · 2022Review
- Article
- Opportunities and challenges of using social media big data to assess mental health consequences of the COVID-19 crisis and future major events.Discover mental health · 2022Article
- Associations between postpartum depression and assistance with household tasks and childcare during the COVID-19 pandemic: evidence from American mothers.BMC pregnancy and childbirth · 2021Article
- Understanding the emotional response to COVID-19 information in news and social media: A mental health perspective.Human behavior and emerging technologies · 2021Review
- A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness.JMIR medical informatics · 2021Article
- The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city.Land use policy · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.