Published on in Vol 22, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19455, first published .
Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis

Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis

Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis

Authors of this article:

Wonkwang Jo 1 Author Orcid Image ;   Jaeho Lee 2 Author Orcid Image ;   Junli Park 3 Author Orcid Image ;   Yeol Kim 2, 3 Author Orcid Image

Journals

  1. Xia H, An W, Li J, Zhang Z. Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data. Socio-Economic Planning Sciences 2022;80:100941 View
  2. Chang A, Schulz P, Tu S, Liu M. Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques. Journal of Medical Internet Research 2020;22(11):e21504 View
  3. Alshalan R, Al-Khalifa H, Alsaeed D, Al-Baity H, Alshalan S. Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach. Journal of Medical Internet Research 2020;22(12):e22609 View
  4. 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
  5. Shi W, Liu D, Yang J, Zhang J, Wen S, Su J. Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter. International Journal of Environmental Research and Public Health 2020;17(22):8701 View
  6. Wang C, Chudzicka-Czupała A, Tee M, Núñez M, Tripp C, Fardin M, Habib H, Tran B, Adamus K, Anlacan J, García M, Grabowski D, Hussain S, Hoang M, Hetnał M, Le X, Ma W, Pham H, Reyes P, Shirazi M, Tan Y, Tee C, Xu L, Xu Z, Vu G, Zhou D, Chan N, Kuruchittham V, McIntyre R, Ho C, Ho R, Sears S. A chain mediation model on COVID-19 symptoms and mental health outcomes in Americans, Asians and Europeans. Scientific Reports 2021;11(1) View
  7. Älgå A, Eriksson O, Nordberg M. Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study. Journal of Medical Internet Research 2020;22(11):e21559 View
  8. Sycinska-Dziarnowska M, Paradowska-Stankiewicz I. Dental Challenges and the Needs of the Population during the Covid-19 Pandemic Period. Real-Time Surveillance Using Google Trends. International Journal of Environmental Research and Public Health 2020;17(23):8999 View
  9. Bae S, (Christine) Sung E, Kwon O. Accounting for social media effects to improve the accuracy of infection models: combatting the COVID-19 pandemic and infodemic. European Journal of Information Systems 2021;30(3):342 View
  10. Cai Z, Zheng S, Huang Y, Au W, Qiu Z, Wu K. The Interactive Effects of Cognition on Coping Styles among Chinese during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2021;18(6):3148 View
  11. Nsoesie E, Cesare N, Müller M, Ozonoff A. COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study. Journal of Medical Internet Research 2020;22(12):e24425 View
  12. Shah A, Naqvi R, Jeong O. Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets. International Journal of Environmental Research and Public Health 2021;18(9):4743 View
  13. Stern J, Georgsson S, Carlsson T. Quality of web-based information at the beginning of a global pandemic: a cross-sectional infodemiology study investigating preventive measures and self care methods of the coronavirus disease 2019. BMC Public Health 2021;21(1) View
  14. Casado-Aranda L, Sánchez-Fernández J, Bastidas-Manzano A. Tourism research after the COVID-19 outbreak: Insights for more sustainable, local and smart cities. Sustainable Cities and Society 2021;73:103126 View
  15. Jong W, Liang O, Yang C. The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis. JMIRx Med 2021;2(3):e27485 View
  16. Chen Y, Xu H, Liu C, Zhang J, Guo C. Association Between Future Orientation and Anxiety in University Students During COVID-19 Outbreak: The Chain Mediating Role of Optimization in Primary-Secondary Control and Resilience. Frontiers in Psychiatry 2021;12 View
  17. Kang K, Han S, Chun J, Kim H. Research trends related to childhood and adolescent cancer survivors in South Korea using word co-occurrence network analysis. Child Health Nursing Research 2021;27(3):201 View
  18. Yang Y, Hsu J, Löfgren K, Cho W. Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining. Social Network Analysis and Mining 2021;11(1) View
  19. Rizzato V, Lotto M, Lourenço Neto N, Oliveira T, Cruvinel T. Digital surveillance: The interests in toothache‐related information after the outbreak of COVID‐19. Oral Diseases 2022;28(S2):2432 View
  20. Al-nuwaiser W. Effect of visual imagery in COVID-19 social media posts on users’ perception. PeerJ Computer Science 2022;8:e1153 View
  21. Bağcı N, Peker I. Interest in dentistry in early months of the COVID‐19 global pandemic: A Google Trends approach. Health Information & Libraries Journal 2022;39(3):284 View
  22. Park E, Kim W, Kim S. What Topics Do Members of the Eating Disorder Online Community Discuss and Empathize with? An Application of Big Data Analytics. Healthcare 2022;10(5):928 View
  23. Kang K, Yu S, Kim C, Lee M, Kim S, Kwon S, Kim S, Kim H, Park M, Choi S. Nurses’ Perceived Needs and Barriers Regarding Pediatric Palliative Care: A Mixed-Methods Study. The Korean Journal of Hospice and Palliative Care 2022;25(2):85 View
  24. Schmid S, Hartwig K, Cieslinski R, Reuter C. Digital Resilience in Dealing with Misinformation on Social Media during COVID-19. Information Systems Frontiers 2024;26(2):477 View
  25. Wang A, Lan J, Wang M, Yu C. The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study. JMIR Medical Informatics 2021;9(11):e30467 View
  26. Altuntas F, Altuntas S, Dereli T. Social network analysis of tourism data: A case study of quarantine decisions in COVID-19 pandemic. International Journal of Information Management Data Insights 2022;2(2):100108 View
  27. Kwon N, Oh J, Ha E. Topic and Trends of Public Perception and Sentiments of COVID-19 Pandemic in South Korea: A Text Mining Approach. The Ewha Medical Journal 2022;45(2):46 View
  28. Kim H, Kang K, Han S, Chun J. Web-Based Research Trends on Child and Adolescent Cancer Survivors Over the Last 5 Years: Text Network Analysis and Topic Modeling Study. Journal of Medical Internet Research 2022;24(2):e32309 View
  29. Onder M, Zengin O. Quality of healthcare information on YouTube: psoriatic arthritis. Zeitschrift für Rheumatologie 2023;82(S1):30 View
  30. Yang Y, Ta N, Li K, Jiao F, Hu B, Li Z. Influential Factors on Collective Anxiety of Online Topic-Based Communities. Frontiers in Psychology 2021;12 View
  31. Fresneda J, Hui J, Hill C. Market segmentation in the emoji era. Communications of the ACM 2022;65(4):105 View
  32. Stern J, Georgsson S, Carlsson T. Quality of web-based information about the coronavirus disease 2019: a rapid systematic review of infodemiology studies published during the first year of the pandemic. BMC Public Health 2022;22(1) View
  33. Jo W, Kim Y, Seo M, Lee N, Park J. Online information analysis on pancreatic cancer in Korea using structural topic model. Scientific Reports 2022;12(1) View
  34. Li W, Zeng F, Zhou W, Chen Z. Internet Public Opinion Diffusion Mechanism in Public Health Emergencies: Based on Entropy Flow Analysis and Dissipative Structure Determination. Frontiers in Public Health 2021;9 View
  35. Zhuang J, Cai G, Lu Y, Xu X, Lin Y, Wong L, Hu Z, Yamamoto T, Morita K, Aoyagi K, He F. Exploring Factors and Associate Responses for Anxiety in the Coronavirus Disease 2019 Pandemic: A Web-Based Survey in Japan. Frontiers in Psychology 2022;12 View
  36. Boukobza A, Burgun A, Roudier B, Tsopra R. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set. JMIR Medical Informatics 2022;10(5):e34306 View
  37. Luo C, Ji K, Tang Y, Du Z. Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach. Journal of Medical Internet Research 2021;23(8):e30715 View
  38. Choi W, Han J, Hong H. Association Between Internet Searches Related to Suicide/Self-harm and Adolescent Suicide Death in South Korea in 2016-2020: Secondary Data Analysis. Journal of Medical Internet Research 2023;25:e46254 View
  39. Zhu J, Li Z, Zhang X, Zhang Z, Hu B. Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis. Journal of Medical Internet Research 2023;25:e45777 View
  40. Ahmad M, Almuteri T, Alharbi A, Tawakul A, Alturiqy M, Alzahrani M, Almutairi S, Almutairi G, Alotaibi A, Almutairi N, Alhabdan L, Alghuyaythat W. Awareness and Acceptance for COVID-19 Booster Dose Vaccination among Residents of Saudi Arabia: Findings of a Cross-Sectional Study. Vaccines 2023;11(5):929 View
  41. Son J, Han J, Kim S, Choi W, Hong H. Korean adolescent suicide and search volume for “self-injury” on internet search engines. Frontiers in Psychiatry 2023;14 View
  42. Byers M, Trahan M, Nason E, Eigege C, Moore N, Washburn M, Metsis V. Detecting Intensity of Anxiety in Language of Student Veterans with Social Anxiety Using Text Analysis. Journal of Technology in Human Services 2023;41(2):125 View
  43. Bondaronek P, Papakonstantinou T, Stefanidou C, Chadborn T. User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults. Public Health in Practice 2023;6:100401 View
  44. Zhou C, Zhang S, Zhao M, Wang L, Chen J, Liu B. Investigating the dynamicity of sentiment predictors in urban green spaces: A machine learning-based approach. Urban Forestry & Urban Greening 2023;89:128130 View
  45. Lan D, Ren W, Ni K, Zhu Y. Topic and Trend Analysis of Weibo Discussions About COVID-19 Medications Before and After China’s Exit from the Zero-COVID Policy: Retrospective Infoveillance Study. Journal of Medical Internet Research 2023;25:e48789 View
  46. Fu H, Oh S. Topics of questions and community interaction in social Q&A during the COVID‐19 pandemic. Health Information & Libraries Journal 2023;40(4):417 View
  47. Cavallaro C, Crespi C, Cutello V, Pavone M, Zito F. Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem. Algorithms 2024;17(2):63 View
  48. Claes M, Farooq U, Salman I, Teern A, Isomursu M, Halonen R. Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic. SN Computer Science 2024;5(2) View
  49. Kang E, Ju H, Kim S, Choi J. Contents analysis of thyroid cancer-related information uploaded to YouTube by physicians in Korea: endorsing thyroid cancer screening, potentially leading to overdiagnosis. BMC Public Health 2024;24(1) View