Published on in Vol 22, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18897, first published .
Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Authors of this article:

Han Woo Park 1, 2 Author Orcid Image ;   Sejung Park 3 Author Orcid Image ;   Miyoung Chong 4 Author Orcid Image

Journals

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  6. Gozzi N, Tizzani M, Starnini M, Ciulla F, Paolotti D, Panisson A, Perra N. Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis. Journal of Medical Internet Research 2020;22(10):e21597 View
  7. Al-Rawi A, Shukla V. Bots as Active News Promoters: A Digital Analysis of COVID-19 Tweets. Information 2020;11(10):461 View
  8. Alnajashi H, Jabbad R, Lavorgna L. Behavioral practices of patients with multiple sclerosis during Covid-19 pandemic. PLOS ONE 2020;15(10):e0241103 View
  9. Tsao S, Chen H, Tisseverasinghe T, Yang Y, Li L, Butt Z. What social media told us in the time of COVID-19: a scoping review. The Lancet Digital Health 2021;3(3):e175 View
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  12. Boon-Itt S, Skunkan Y. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health and Surveillance 2020;6(4):e21978 View
  13. Dalili Shoaei M, Dastani M. The Role of Twitter During the COVID-19 Crisis: A Systematic Literature Review. Acta Informatica Pragensia 2020;9(2):154 View
  14. Martínez-Cardama S, Pacios A. Twitter communication of university libraries in the face of Covid-19. El profesional de la información 2020 View
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  17. Chandrasekaran R, Mehta V, Valkunde T, Moustakas E. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. Journal of Medical Internet Research 2020;22(10):e22624 View
  18. Laudanski K, Shea G, DiMeglio M, Restrepo M, Solomon C. What Can COVID-19 Teach Us about Using AI in Pandemics?. Healthcare 2020;8(4):527 View
  19. Lyu J, Luli G. Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study. Journal of Medical Internet Research 2021;23(2):e25108 View
  20. de Melo T, Figueiredo C. Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach. JMIR Public Health and Surveillance 2021;7(2):e24585 View
  21. Pascual-Ferrá P, Alperstein N, Barnett D. Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication. Disaster Medicine and Public Health Preparedness 2020:1 View
  22. Gencoglu O. Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19. Machine Learning and Knowledge Extraction 2020;2(4):603 View
  23. Shah A, Yan X, Qayyum A, Naqvi R, Shah S. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. International Journal of Medical Informatics 2021;149:104434 View
  24. Al-Laith A, Alenezi M. Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic. Information 2021;12(2):86 View
  25. Duong T, Pham K, Do B, Kim G, Dam H, Le V, Nguyen T, Nguyen H, Nguyen T, Le T, Do H, Yang S. Digital Healthy Diet Literacy and Self-Perceived Eating Behavior Change during COVID-19 Pandemic among Undergraduate Nursing and Medical Students: A Rapid Online Survey. International Journal of Environmental Research and Public Health 2020;17(19):7185 View
  26. Kim H, Choi E, Park S, Kim E. Factors Influencing Preventive Behavior against Coronavirus Disease 2019 (COVID-19) among Medically Inclined College Students. Journal of Korean Academy of Fundamentals of Nursing 2020;27(4):428 View
  27. Petersen K, Gerken J. #Covid-19: An exploratory investigation of hashtag usage on Twitter. Health Policy 2021;125(4):541 View
  28. Chang C, Monselise M, Yang C. What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter. Journal of Healthcare Informatics Research 2021;5(1):70 View
  29. Al-Khalifa K, AlSheikh R, Alsahafi Y, Alkhalifa A, Sadaf S, Muazen Y, Al-Moumen S, Yermal A. Dental care during the COVID-19 Pandemic: An Arabic tweets analysis (Preprint). JMIR Public Health and Surveillance 2020 View
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  33. Park S, Han S, Kim J, Molaie M, Vu H, Singh K, Han J, Lee W, Cha M. COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication. Journal of Medical Internet Research 2021;23(3):e23272 View
  34. Schück S, Foulquié P, Mebarki A, Faviez C, Khadhar M, Texier N, Katsahian S, Burgun A, Chen X. Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling. JMIR Formative Research 2021;5(4):e23593 View
  35. Banerjee D, Meena K. COVID-19 as an “Infodemic” in Public Health: Critical Role of the Social Media. Frontiers in Public Health 2021;9 View
  36. Palazzi M, Solé-Ribalta A, Calleja-Solanas V, Meloni S, Plata C, Suweis S, Borge-Holthoefer J. An ecological approach to structural flexibility in online communication systems. Nature Communications 2021;12(1) View
  37. McKay K, Wayland S, Ferguson D, Petty J, Kennedy E. “At Least until the Second Wave Comes…”: A Twitter Analysis of the NHS and COVID-19 between March and June 2020. International Journal of Environmental Research and Public Health 2021;18(8):3943 View
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Books/Policy Documents

  1. Shah C, Sebastian M. Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. View
  2. Sabuncu I, Aydin M. Data Science Advancements in Pandemic and Outbreak Management. View