Published on in Vol 6, No 3 (2020): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20493, first published .
Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study

Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study

Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study

Journals

  1. Syeda H, Syed M, Sexton K, Syed S, Begum S, Syed F, Prior F, Yu Jr F. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Medical Informatics 2021;9(1):e23811 View
  2. Tayarani N. M. Applications of artificial intelligence in battling against covid-19: A literature review. Chaos, Solitons & Fractals 2021;142:110338 View
  3. Wang H, Li Y, Hutch M, Naidech A, Luo Y. Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study. Journal of Medical Internet Research 2021;23(2):e26302 View
  4. Bechard L, Bergelt M, Neudorf B, DeSouza T, Middleton L. Using the Health Belief Model to Understand Age Differences in Perceptions and Responses to the COVID-19 Pandemic. Frontiers in Psychology 2021;12 View
  5. van den Hurk K, Merz E, Prinsze F, Spekman M, Quee F, Ramondt S, Slot E, Vrielink H, Huis in ’t Veld E, Zaaijer H, Hogema B. Low awareness of past SARS-CoV-2 infection in healthy plasma donors. Cell Reports Medicine 2021;2(3):100222 View
  6. Jony S, Haque U, Webb N, Spence E, Rahman M, Aghamohammadi N, Lie Y, Angulo-Molina A, Ananth S, Ren X, Kawachi N, Ito H, Ulvi O, Lubinda J, Karamehic-Muratovic A, Maher W, Ali P, Rahman M. Analyzing Predictors of Control Measures and Psychosocial Problems Associated with COVID-19 Pandemic: Evidence from Eight Countries. Behavioral Sciences 2021;11(8):106 View
  7. Alyasseri Z, Al‐Betar M, Doush I, Awadallah M, Abasi A, Makhadmeh S, Alomari O, Abdulkareem K, Adam A, Damasevicius R, Mohammed M, Zitar R. Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches. Expert Systems 2022;39(3) View
  8. Chadaga K, Prabhu S, Vivekananda B, Niranjana S, Umakanth S, Pham D. Battling COVID-19 using machine learning: A review. Cogent Engineering 2021;8(1) View
  9. Bottemanne H, Friston K. An active inference account of protective behaviours during the COVID-19 pandemic. Cognitive, Affective, & Behavioral Neuroscience 2021;21(6):1117 View
  10. Tawfik A, El Desouky E, Salem M. Egyptians’ Perceptions of COVID-19: Applying the Health Belief Model: A Cross-sectional Study. Open Access Macedonian Journal of Medical Sciences 2022;10(E):1397 View
  11. Hansen A, Farewell C, Jewell J, Leiferman J. Exploring Predictors of Social Distancing Compliance in the United States during the COVID-19 Pandemic. Disaster Medicine and Public Health Preparedness 2023;17 View
  12. Daghriri T, Proctor M, Matthews S. Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration. International Journal of Environmental Research and Public Health 2022;19(6):3230 View
  13. Gunasekeran D, Chew A, Chandrasekar E, Rajendram P, Kandarpa V, Rajendram M, Chia A, Smith H, Leong C. The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review. Journal of Medical Internet Research 2022;24(4):e33680 View
  14. Goh H, Ho C, Abas F. Front-end deep learning web apps development and deployment: a review. Applied Intelligence 2023;53(12):15923 View
  15. Zadorozhna V, Sergeyeva T, Maksymenok O, Protas S, Hrynchuk G, Rodyna N. PREVALENCE OF MARKERS OF SARS-COV-2 INFECTION AMONG RESIDENTS OF KYIV REGION AT THE BEGINNING OF THE COVID-19 EPIDEMIC – FIRST RESULTS IN UKRAINE. JOURNAL OF THE NATIONAL ACADEMY OF MEDICAL SCIENCES OF UKRAINE 2021;(2;2021):118 View
  16. Heer R, Sandhu P, Wenban C, Mandal A, Missouris C. Vitamin D in the news: A call for clear public health messaging during Covid-19. Nutrition and Health 2022;28(4):733 View
  17. Guidry J, O’Donnell N, Meganck S, Lovari A, Miller C, Messner M, Hill A, Medina-Messner V, Carlyle K. Tweeting a Pandemic: Communicating #COVID19 Across the Globe. Health Communication 2023;38(11):2377 View
  18. Powell S, Bushman B, Goddard S, Scace D, Klabunde R. Stress and Physical Activity in COVID-19 Exploratory Study (SPACES). International Journal of Kinesiology in Higher Education 2023;7(2):95 View
  19. MacKay M, Ford C, Colangeli T, Gillis D, McWhirter J, Papadopoulos A. A content analysis of Canadian influencer crisis messages on Instagram and the public’s response during COVID-19. BMC Public Health 2022;22(1) View
  20. Heydari A, Isfahani P, Bagheri S. Predicting Covid-19 preventive behaviors based on constructs of health belief model. Primary Health Care Research & Development 2023;24 View
  21. Yu Y, Ling R, Ip T, Luo S, Lau J. Factors of COVID-19 Vaccination among Hong Kong Chinese Men Who Have Sex with Men during Months 5–8 since the Vaccine Rollout—General Factors and Factors Specific to This Population. Vaccines 2022;10(10):1763 View
  22. Dong X, Lian Y. The moderating effects of entertainers on public engagement through government activities in social media during the COVID-19. Telematics and Informatics 2022;66:101746 View
  23. Tajiki I, Vizeshfar F, Keshtkaran Z. The effect of training program based on health belief model on burn prevention knowledge in mothers of children aged to 1–3 years: A randomized controlled. Burns 2022;48(4):808 View
  24. Deveci S, Cevik C, Baydur H, Onsuz F, Tosun S, Ergor A. Validity and Reliability of the COVID-19 Knowledge, Attitude and Behavior Scale. Vaccines 2023;11(2):317 View
  25. Wang D, Lu J. How News Agencies’ Twitter Posts on COVID-19 Vaccines Attract Audiences’ Twitter Engagement: A Content Analysis. International Journal of Environmental Research and Public Health 2022;19(5):2716 View
  26. Lelisho M, Pandey D, Alemu B, Pandey B, Tareke S. The Negative Impact of Social Media during COVID-19 Pandemic. Trends in Psychology 2022;31(1):123 View
  27. Li S, Liu Y, Kumar V. Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation. Computational Intelligence and Neuroscience 2022;2022:1 View
  28. Ke S, Neeley-Tass E, Barnes M, Hanson C, Giraud-Carrier C, Snell Q. COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach. JMIR Infodemiology 2022;2(2):e37861 View
  29. Joseph J, Mohd Zawawi J, Ahmad Ghazali A. MEDIA AND HEALTH COMMUNICATION OF COVID-19 TOWARDS HEALTH BELIEF MODEL. Asian Journal of Applied Communication 2022;12(S1):61 View
  30. Myneni S, Cuccaro P, Montgomery S, Pakanati V, Tang J, Singh T, Dominguez O, Cohen T, Reininger B, Savas L, Fernandez M. Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science. JMIR Infodemiology 2023;3:e40156 View
  31. Singh T, Roberts K, Cohen T, Cobb N, Franklin A, Myneni S. Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework. Journal of Biomedical Informatics 2023;140:104324 View
  32. Kour H, Gupta M. AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19. Neural Processing Letters 2023;55(3):2265 View
  33. Sarirete A. Sentiment analysis tracking of COVID-19 vaccine through tweets. Journal of Ambient Intelligence and Humanized Computing 2023;14(11):14661 View
  34. Purnama S, Susanna D, Achmadi U, Eryando T, Wulandari L. Attitude towards dengue control efforts with the potential of digital technology during COVID-19: partial least squares-structural equation modeling. F1000Research 2022;11:1283 View
  35. Saeedvand S, Jafari M, Aghdasi H, Baltes J, Rahmani A. Deep learning: A taxonomy of modern weapons to combat Covid‐19 similar pandemics in smart cities. Concurrency and Computation: Practice and Experience 2022;34(27) View
  36. Ukonu M, Mbamalu M. Predictors of Compliance to COVID-19 Containment Communications in Nigeria’s Federal Capital Territory, Enugu, and Lagos States. SAGE Open 2021;11(3):215824402110472 View
  37. Bak M, Chiu C, Chin J. Mental Health Pandemic During the COVID-19 Outbreak: Social Media As a Window to Public Mental Health. Cyberpsychology, Behavior, and Social Networking 2023;26(5):346 View
  38. Purnama S, Susanna D, Achmadi U, Eryando T. Attitude towards dengue control efforts with the potential of digital technology during COVID-19: partial least squares-structural equation modeling. F1000Research 2023;11:1283 View
  39. Taheri Chorsi S, Moqaddam M, Jafari Varjoshani N, Ahmadi F. Predicting Covid-19 Prevention Behaviors Based on the Health Belief Model Among the Students of Nursing and Midwifery Faculty of Zanjan City. Preventive Care In Nursing and Midwifery Journal 2023;13(3):70 View
  40. Balogun B, Hogden A, Kemp N, Yang L, Agaliotis M. Public health agencies’ use of social media for communication during pandemics: a scoping review of the literature. Osong Public Health and Research Perspectives 2023;14(4):235 View
  41. Khani Jeihooni A, Namdari A, Kashfi S, Kamyab A, Harsini P, Rakhshani T. Effects of an educational intervention based on the health belief model on COVID-19 preventive behaviors among health personnel in Abadan, Iran. Journal of Public Health 2023 View
  42. Gumasing M, Ong A, Sy M, Prasetyo Y, Persada S. A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines. Heliyon 2023;9(10):e20644 View
  43. Zareipour M, Ardakani M, Moradali M, Jadgal M, Movahed E. Determinants of COVID-19 Prevention Behavior in the Elderly in Urmia: Application of Health Belief Model. Open Access Macedonian Journal of Medical Sciences 2020;8(T1):646 View
  44. Zhang L, Harris Ao S, Francis Ye J, Zhao X. How does health communication on social media influence e-cigarette perception and use? A trend analysis from 2017 to 2020. Addictive Behaviors 2024;149:107875 View
  45. P. R. Y, Mehra A. Promoting COVID-19 vaccine acceptance: analysis of media messaging in India. Media Asia 2024;51(3):420 View
  46. Chandrasekaran R, Konaraddi K, Sharma S, Moustakas E. Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube. Journal of Medical Systems 2024;48(1) View
  47. Feldmane S, Mārtinsone K, Perepjolkina V. Using the Health Belief Model to Predict Vaccination Intention in Unvaccinated Adults in Latvia During the COVID-19 Pandemic. Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences. 2024;78(1):66 View
  48. Zhang L, Ye J, Zhao X. “I Saw it Incidentally but Frequently”: Exploring the Effects of Online Health Information Scanning on Lung Cancer Screening Behaviors Among Chinese Smokers. Health Communication 2024:1 View
  49. Singh K, Kaur N, Prabhu A. Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review. Current Topics in Medicinal Chemistry 2024;24(8):737 View

Books/Policy Documents

  1. Wright K. Communicating Science in Times of Crisis. View
  2. Gupta R, Gupta A, Bedi M, Pal S. Understanding COVID-19: The Role of Computational Intelligence. View
  3. Lyu H, Vo Quang A. Singapore's First Year of COVID-19. View
  4. Modegh R, Salimi A, Ilami S, Dehqan A, Dashti H, Javanmard S, Ghanaati H, Rabiee H. The Science behind the COVID Pandemic and Healthcare Technology Solutions. View
  5. Mallikarjuna B, D. J. A, M. S, Sabharwal M. Handbook of Research on Advances in Data Analytics and Complex Communication Networks. View
  6. Iyer S, Gejji S, Pandya R. Handbook of Research on Lifestyle Sustainability and Management Solutions Using AI, Big Data Analytics, and Visualization. View
  7. Bondugula R, Udgata S, Rahman N, Sivangi K. Edge-of-Things in Personalized Healthcare Support Systems. View
  8. Ophir Y, Liu A, Shah P, Wang R, Acosta N, Gillis S. Palgrave Handbook of Science and Health Journalism. View
  9. Sarker A. Natural Language Processing in Biomedicine. View