This paper is in the following e-collection/theme issue:

Natural Language Processing (692) JMIR Theme Issue: COVID-19 Special Issue (2452) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Infodemiology and Infoveillance (1349) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (383)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22734, first published .
Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach

Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach

Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach

Authors of this article:

Oladapo Oyebode1 Author Orcid Image ;   Chinenye Ndulue1 Author Orcid Image ;   Ashfaq Adib1 Author Orcid Image ;   Dinesh Mulchandani1 Author Orcid Image ;   Banuchitra Suruliraj1 Author Orcid Image ;   Fidelia Anulika Orji2 Author Orcid Image ;   Christine T Chambers3, 4 Author Orcid Image ;   Sandra Meier5 Author Orcid Image ;   Rita Orji1 Author Orcid Image
Article Authors Cited by (41) Tweetations (15) Metrics

Journals

  1. CARDINALE M. Preparing athletes and staff for the first "pandemic" Olympic Games. The Journal of Sports Medicine and Physical Fitness 2021;61(8) View
  2. Marcec R, Likic R. Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgraduate Medical Journal 2022;98(1161):544 View
  3. Teague S, Shatte A, Weller E, Fuller-Tyszkiewicz M, Hutchinson D. Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review. JMIR Mental Health 2022;9(2):e33058 View
  4. Al-Garadi M, Yang Y, Sarker A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare 2022;10(11):2270 View
  5. Oyebode O, Ndulue C, Mulchandani D, Suruliraj B, Adib A, Orji F, Milios E, Matwin S, Orji R. COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing. Journal of Healthcare Informatics Research 2022;6(2):174 View
  6. Qorib M, Oladunni T, Denis M, Ososanya E, Cotae P. Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset. Expert Systems with Applications 2023;212:118715 View
  7. Oyebode O, Orji R. Identifying adverse drug reactions from patient reviews on social media using natural language processing. Health Informatics Journal 2023;29(1) View
  8. Bakuri A, Antwi-Berko D. “What Other Information Is There?”: Identifying Information Gaps, Perceptions and Misconceptions on COVID-19 Among Minority Ethnic Groups in the Netherlands. Frontiers in Health Services 2022;2 View
  9. Chen Z, Kwak D. It’s Okay to be Not Okay: An Analysis of Twitter Responses to Naomi Osaka’s Withdrawal due to Mental Health Concerns. Communication & Sport 2023;11(3):439 View
  10. Huangfu L, Mo Y, Zhang P, Zeng D, He S. COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment–Based Topic Modeling. Journal of Medical Internet Research 2022;24(2):e31726 View
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  12. Bhuptani P, Hunter J, Goodwin C, Millman C, Orchowski L. Characterizing Intimate Partner Violence in the United States During the COVID-19 Pandemic: A Systematic Review. Trauma, Violence, & Abuse 2023;24(5):3220 View
  13. Gómez-Salgado J, Palomino-Baldeón J, Ortega-Moreno M, Fagundo-Rivera J, Allande-Cussó R, Ruiz-Frutos C. COVID-19 information received by the Peruvian population, during the first phase of the pandemic, and its association with developing psychological distress. Medicine 2022;101(5):e28625 View
  14. Weger R, Lossio-Ventura J, Rose-McCandlish M, Shaw J, Sinclair S, Pereira F, Chung J, Atlas L. 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 2023;10:e40899 View
  15. Oduntan A, Oyebode O, Beltran A, Fowles J, Steeves D, Orji R. “I Let Depression and Anxiety Drown Me…”: Identifying Factors Associated With Resilience Based on Journaling Using Machine Learning and Thematic Analysis. IEEE Journal of Biomedical and Health Informatics 2022;26(7):3397 View
  16. Wang Y, Croucher S, Pearson E. National Leaders’ Usage of Twitter in Response to COVID-19: A Sentiment Analysis. Frontiers in Communication 2021;6 View
  17. Castilla-Puentes R, Pesa J, Brethenoux C, Furey P, Gil Valletta L, Falcone T. 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 2022;6(6):e33637 View
  18. Gélinas-Gascon F, Khoury R. Modeling and Moderation of COVID-19 Social Network Chat. Information 2023;14(2):124 View
  19. Nia Z, Ahmadi A, Bragazzi N, Woldegerima W, Mellado B, Wu J, Orbinski J, Asgary A, Kong J, Valls Martínez M. A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments. PLOS ONE 2022;17(8):e0272208 View
  20. Nia Z, Ahmadi A, Bragazzi N, Woldegerima W, Mellado B, Wu J, Orbinski J, Asgary A, Kong J. A Cross-Country Analysis of Macroeconomic Responses to COVID-19 Pandemic Using Twitter Sentiments. SSRN Electronic Journal 2022 View
  21. Danesh F, Dastani M. Text classification technique for discovering country-based publications from international COVID-19 publications. DIGITAL HEALTH 2023;9 View
  22. Kalabikhina I, Zubova E, Loukachevitch N, Kolotusha A, Kazbekova Z, Banin E, Klimenko G. Identifying Reproductive Behavior Arguments in Social Media Content Users’ Opinions through Natural Language Processing Techniques. Population and Economics 2023;7(2):40 View
  23. Dainty K, Seaton M, O’Neill B, Mohindra R. Going home positive: a qualitative study of the experiences of care for patients with COVID-19 who are not hospitalized. CMAJ Open 2023;11(6):E1041 View
  24. Michalski A, Lis K, Stankiewicz J, Kloska S, Sycz A, Dudziński M, Muras-Szwedziak K, Nowicki M, Bazan-Socha S, Dabrowski M, Basak G. Supporting the Diagnosis of Fabry Disease Using a Natural Language Processing-Based Approach. Journal of Clinical Medicine 2023;12(10):3599 View
  25. Reisinezhad P, Fakhrahmad M. Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining. Kybernetes 2023;52(7):2507 View
  26. Liu Y, Shi J, Zhao C, Zhang C. Generalizing factors of COVID-19 vaccine attitudes in different regions: A summary generation and topic modeling approach. DIGITAL HEALTH 2023;9 View
  27. Barrera R, Poindexter K, Tucker C, Winkler M, Dashti H. Amplifying the lived experiences of parenteral nutrition consumers through the thematic analysis of social media posts. Nutrition in Clinical Practice 2024;39(4):850 View
  28. Suwida K, Kardawi M, Purwitasari D, Mabahist F. A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates. EMITTER International Journal of Engineering Technology 2023:89 View
  29. Kumar V, Sahoo A, Balasubramanian S, Gholston S. Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data. International Journal of Production Research 2024:1 View
  30. Han L, Gladkoff S, Erofeev G, Sorokina I, Galiano B, Nenadic G. Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning. Frontiers in Digital Health 2024;6 View
  31. Omisore O, Odenigbo I, Orji J, Beltran A, Meier S, Baghaei N, Orji R. Extended Reality for Mental Health Evaluation: Scoping Review. JMIR Serious Games 2024;12:e38413 View
  32. Jordan A, Park A. Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content. JMIR AI 2024;3:e54501 View
  33. Whitfield C, Liu Y, Anwar M. 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 2024 View
  34. Wu D, Ng M, Gupta S, Raynor P, Tao Y, Ren Y, Hung P, Qiao S, Zhang J, Fillo J, Li X, Guille C, Eichelberger K, Olatosi B. 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 2024;7:e52735 View
  35. Rajeshkannan N, Somasundaram D, Kumarendran B, Kumanan T, Aloysius I, Sugirthan N, Sasikumar S, Jegathesan N. The mental health impact of COVID-19: the need for community interventions and collaboration for recovery. Academia Medicine 2024;1(1) View
  36. Holmes A, Sachar A, Chang Y. Perceived Impact of COVID‐19 in an Underserved Community: A Natural Language Processing Approach. Journal of Advanced Nursing 2024 View

Books/Policy Documents

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