ArticleJMIR medical informatics2021
Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 61 citations in OpenAlex.
- Article
- Public representations of the nursing image in YouTube comments: analysis based on Jean Watson's theory.Revista da Escola de Enfermagem da U S P · 2026Article
- Surveillance of Twitter Data on COVID-19 Symptoms During the Omicron Variant Period: A Sentiment Analysis.JMIR formative research · 2025Article
- Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing.Journal of racial and ethnic health disparities · 2025Article
- Identification of emerging harms due to COVID-19 outbreak: a qualitative study in Iran.BMC public health · 2025Article
- Disclosure Patterns of Opioid Use Disorders in Perinatal Care During the Opioid Epidemic on X From 2019 to 2021: Thematic Analysis.JMIR pediatrics and parenting · 2024Article
- Extended Reality for Mental Health Evaluation: Scoping Review.JMIR serious games · 2024Article
- Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content.JMIR AI · 2024Article
- Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning.Frontiers in digital health · 2024Article
- Supporting the Diagnosis of Fabry Disease Using a Natural Language Processing-Based Approach.Journal of clinical medicine · 2023Article
- COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.International journal of environmental research and public health · 2023Article
- Trends in Language Use During the COVID-19 Pandemic and Relationship Between Language Use and Mental Health: Text Analysis Based on Free Responses From a Longitudinal Study.JMIR mental health · 2023Article
- Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset.Expert systems with applications · 2023Article
- The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.Healthcare (Basel, Switzerland) · 2022Review
- Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis.JMIR formative research · 2022Article
- COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing.Journal of healthcare informatics research · 2022Article
- Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review.JMIR mental health · 2022Article
- COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling.Journal of medical Internet research · 2022Article
- Article
- "Frontiers in health services · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally.
objectiveThis paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data.
methodsWe applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes.
resultsA total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research.
conclusionsWe uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.
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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.