Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22590, first published .
Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence

Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence

Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence

Journals

  1. Xue J, Chen J, Chen C, Hu R, Zhu T. The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets. Journal of Medical Internet Research 2020;22(11):e24361 View
  2. Zeng W, Gautam A, Huson D. On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19. Computation 2021;9(1):4 View
  3. 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
  4. Soiné H, Kriegel L, Dollmann J. The impact of the COVID-19 pandemic on risk perceptions: differences between ethnic groups in Germany. European Societies 2021;23(sup1):S289 View
  5. Wang Y, Wu P, Liu X, Li S, Zhu T, Zhao N. Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis. Journal of Medical Internet Research 2020;22(12):e24775 View
  6. Hussain A, Tahir A, Hussain Z, Sheikh Z, Gogate M, Dashtipour K, Ali A, Sheikh A. Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study. Journal of Medical Internet Research 2021;23(4):e26627 View
  7. Arora N, Banerjee A, Narasu M. The role of artificial intelligence in tackling COVID-19. Future Virology 2020;15(11):717 View
  8. Adikari A, Nawaratne R, De Silva D, Ranasinghe S, Alahakoon O, Alahakoon D. Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence. Journal of Medical Internet Research 2021;23(4):e27341 View
  9. Pang P, Cai Q, Jiang W, Chan K. Engagement of Government Social Media on Facebook during the COVID-19 Pandemic in Macao. International Journal of Environmental Research and Public Health 2021;18(7):3508 View
  10. Chintalapudi N, Battineni G, Amenta F. Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models. Infectious Disease Reports 2021;13(2):329 View
  11. Thavorn J, Gowanit C, Muangsin V, Muangsin N. Collaboration Network and Trends of Global Coronavirus Disease Research: A Scientometric Analysis. IEEE Access 2021;9:45001 View
  12. Zhang C, Xu S, Li Z, Hu S. Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study. Journal of Medical Internet Research 2021;23(3):e26482 View
  13. Ramamoorthy T, Karmegam D, Mappillairaju B. Use of social media data for disease based social network analysis and network modeling: A Systematic Review. Informatics for Health and Social Care 2021:1 View
  14. Gabarron E, Rivera-Romero O, Miron-Shatz T, Grainger R, Denecke K. Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review. Yearbook of Medical Informatics 2021 View
  15. Jo W, Chang D, You M, Ghim G. A social network analysis of the spread of COVID-19 in South Korea and policy implications. Scientific Reports 2021;11(1) View
  16. Cuomo R, Purushothaman V, Li J, Cai M, Mackey T. A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States. BMC Public Health 2021;21(1) View
  17. Aiyanyo I, Samuel H, Lim H. Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning. Sustainability 2021;13(9):4986 View
  18. Satu M, Khan M, Mahmud M, Uddin S, Summers M, Quinn J, Moni M. TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowledge-Based Systems 2021:107126 View
  19. 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
  20. Singh R, Singh P, Kumar R, Kabir M, Kamal M, Rauf A, Albadrani G, Sayed A, Mousa S, Abdel-Daim M, Uddin M. Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19. Frontiers in Pharmacology 2021;12 View
  21. Chen J, Wang Y. Social Media Use for Health Purposes: Systematic Review. Journal of Medical Internet Research 2021;23(5):e17917 View

Books/Policy Documents

  1. Madani Y, Erritali M, Bouikhalene B. Business Intelligence. View