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;46(4):443 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;30(01):200 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;226:107126 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
  22. Dutta S, Kumar A, Dutta M, Walsh C, Trinidad Segovia J. Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach. PLOS ONE 2021;16(6):e0252332 View
  23. Lappeman J, Munyai K, Mugo Kagina B. Negative sentiment towards COVID-19 vaccines: A comparative study of USA and UK social media posts before vaccination rollout. F1000Research 2021;10:472 View
  24. Pathik N, Shukla P. An efficient sentiment analysis using topic model based optimized recurrent neural network. International Journal on Smart Sensing and Intelligent Systems 2021;14(1):1 View
  25. Krawczyk K, Chelkowski T, Laydon D, Mishra S, Xifara D, Gibert B, Flaxman S, Mellan T, Schwämmle V, Röttger R, Hadsund J, Bhatt S. Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource. Journal of Medical Internet Research 2021;23(6):e28253 View
  26. Boucher J, Cornelson K, Benham J, Fullerton M, Tang T, Constantinescu C, Mourali M, Oxoby R, Marshall D, Hemmati H, Badami A, Hu J, Lang R. Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study. JMIR Infodemiology 2021;1(1):e28800 View
  27. Cuenca-Zaldívar J, Torrente-Regidor M, Martín-Losada L, Fernández-De-Las-Peñas C, Florencio L, Sousa P, Palacios-Ceña D. Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study. JMIR Medical Informatics 2022;10(5):e38308 View
  28. Marshall C, Lanyi K, Green R, Wilkins G, Pearson F, Craig D. Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study. JMIR Infodemiology 2022;2(1):e32449 View
  29. Ng J, Abdelkader W, Lokker C. Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data. BMC Complementary Medicine and Therapies 2022;22(1) View
  30. Vaishali P, Kumari P. Ensemble learning based classifier to predict depression caused due to pandemic. Journal of Physics: Conference Series 2021;2089(1):012026 View
  31. Pan Y, Zhang L. Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design. Advanced Engineering Informatics 2022;54:101758 View
  32. Cho J, Kang W, Lee J. Korea's Response to COVID-19 According to Set Time Frames, With a Focus on the Network Between the Government and Responding Agencies: Social Network Analysis. JMIR Public Health and Surveillance 2022;8(5):e35958 View
  33. Jain V, Kashyap K. Multilayer hybrid ensemble machine learning model for analysis of Covid-19 vaccine sentiments. Journal of Intelligent & Fuzzy Systems 2022;43(5):6307 View
  34. Marqués-Sánchez P, Pinto-Carral A, Fernández-Villa T, Vázquez-Casares A, Liébana-Presa C, Benítez-Andrades J. Identification of cohesive subgroups in a university hall of residence during the COVID-19 pandemic using a social network analysis approach. Scientific Reports 2021;11(1) View
  35. 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
  36. Wu X, Wang W, Li Q, Peng Z, Zhu J. Current Situation With Organ Donation and Transplantation in China: Application of Machine Learning. Transplantation Proceedings 2022;54(7):1711 View
  37. Heyerdahl L, Lana B, Giles-Vernick T. The Impact of the Online COVID-19 Infodemic on French Red Cross Actors’ Field Engagement and Protective Behaviors: Mixed Methods Study. JMIR Infodemiology 2021;1(1):e27472 View
  38. García-Sánchez F, Cruz-Benito J. Research Advances on User Interactions in Social Media Using Data Science Approaches. Applied Sciences 2022;12(21):10929 View
  39. Chang C, Jen H, Su W. Trends in artificial intelligence in nursing: Impacts on nursing management. Journal of Nursing Management 2022;30(8):3644 View
  40. You G, Gan S, Guo H, Dagestani A. Public Opinion Spread and Guidance Strategy under COVID-19: A SIS Model Analysis. Axioms 2022;11(6):296 View
  41. Marín E, González-Tejero C, García M, García F. Catholic Impact Evolution Through Public Twitter Data During COVID-19. International Journal of Cloud Applications and Computing 2022;12(1):1 View
  42. 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
  43. Lewicka M, Hamilton J, Waters E, Orom H, Schofield E, Kiviniemi M, Kanetsky P, Hay J. Associations between social COVID-19 exposure and psychological functioning. Journal of Behavioral Medicine 2023;46(3):472 View
  44. Kahanek A, Yu X, Hong L, Cleveland A, Philbrick J. Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets. JMIR Infodemiology 2021;1(1):e31671 View
  45. Geronikolou S, Drosatos G, Chrousos G. Emotional Analysis of Twitter Posts During the First Phase of the COVID-19 Pandemic in Greece: Infoveillance Study. JMIR Formative Research 2021;5(9):e27741 View
  46. Kumar V. Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model. Scientific Reports 2022;12(1) View
  47. Amin S, Irfan Uddin M, H. Al-Baity H, Ali Zeb M, Abrar Khan M. Machine Learning Approach for COVID-19 Detection on Twitter. Computers, Materials & Continua 2021;68(2):2231 View
  48. Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. International Journal of Digital Earth 2023;16(1):130 View
  49. Khan J, Khan J, Ali F, Ullah F, Bacha J, Lee S. Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review. IEEE Access 2022;10:62613 View
  50. Kandasamy V, Trojovský P, Machot F, Kyamakya K, Bacanin N, Askar S, Abouhawwash M. Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme. Sensors 2021;21(22):7582 View
  51. Tanhapour M, Safaei A, Shakibian H. Personal health record system based on social network analysis. Multimedia Tools and Applications 2022;81(19):27601 View
  52. Chang V, Ng C, Xu Q, Guizani M, Hossain M. How Do People View COVID-19 Vaccines. Journal of Global Information Management 2022;30(10):1 View
  53. Li W, Deng X, Shao H, Wang X. Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey. Computer Modeling in Engineering & Sciences 2021;129(1):65 View
  54. Jun J, Zain A, Chen Y, Kim S. Adverse Mentions, Negative Sentiment, and Emotions in COVID-19 Vaccine Tweets and Their Association with Vaccination Uptake: Global Comparison of 192 Countries. Vaccines 2022;10(5):735 View
  55. Yum S. Different Characteristics of Social Networks for COVID-19 in Europe. European Review 2022;30(6):749 View
  56. Wang W, Ning H, Shi F, Dhelim S, Zhang W, Chen L. A Survey of Hybrid Human-Artificial Intelligence for Social Computing. IEEE Transactions on Human-Machine Systems 2022;52(3):468 View
  57. Umair A, Masciari E. Sentimental and spatial analysis of COVID-19 vaccines tweets. Journal of Intelligent Information Systems 2023;60(1):1 View
  58. Liu L, Fu Y. Study on the mechanism of public attention to a major event: The outbreak of COVID-19 in China. Sustainable Cities and Society 2022;81:103811 View
  59. Amin S, Alharbi A, Uddin M, Alyami H. Adapting recurrent neural networks for classifying public discourse on COVID-19 symptoms in Twitter content. Soft Computing 2022;26(20):11077 View
  60. Troisi O, Fenza G, Grimaldi M, Loia F. Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic. Computers in Human Behavior 2022;126:106986 View
  61. Bonifazi G, Breve B, Cirillo S, Corradini E, Virgili L. Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach. Information Processing & Management 2022;59(6):103095 View
  62. Chandra R, Krishna A, Cotfas L. COVID-19 sentiment analysis via deep learning during the rise of novel cases. PLOS ONE 2021;16(8):e0255615 View
  63. Abiola O, Abayomi-Alli A, Tale O, Misra S, Abayomi-Alli O. Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser. Journal of Electrical Systems and Information Technology 2023;10(1) View
  64. Arora G, Joshi J, Mandal R, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021;10(8):1048 View
  65. Binkheder S, Aldekhyyel R, AlMogbel A, Al-Twairesh N, Alhumaid N, Aldekhyyel S, Jamal A. Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia. International Journal of Environmental Research and Public Health 2021;18(24):13388 View
  66. Ueda M, Watanabe K, Sueki H. Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm. Journal of Medical Internet Research 2023;25:e44965 View
  67. Taherdoost H, Madanchian M. Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research. Computers 2023;12(2):37 View
  68. Gourisaria M, Chandra S, Das H, Patra S, Sahni M, Leon-Castro E, Singh V, Kumar S. Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies. Healthcare 2022;10(5):881 View
  69. Mir A, Sevukan R. Sentiment analysis of Indian Tweets about Covid-19 vaccines. Journal of Information Science 2022:016555152211180 View
  70. Raheja S, Asthana A. Sentiment Analysis of Tweets During the COVID-19 Pandemic Using Multinomial Logistic Regression. International Journal of Software Innovation 2022;11(1):1 View
  71. 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
  72. Arsiwala-Scheppach L, Chaurasia A, Müller A, Krois J, Schwendicke F. Machine Learning in Dentistry: A Scoping Review. Journal of Clinical Medicine 2023;12(3):937 View
  73. Aljedaani W, Saad E, Rustam F, de la Torre Díez I, Ashraf I. Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends. Mathematics 2022;10(17):3199 View
  74. Wang Y, Zhao Y, Pan Q. Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data. Briefings in Bioinformatics 2022;23(1) View
  75. Dang Q, Li S. Exploring Public Discussions Regarding COVID-19 Vaccinations on Microblogs in China: Findings from Machine Learning Algorithms. International Journal of Environmental Research and Public Health 2022;19(20):13476 View
  76. Lee E, Zheng H, Goh D, Lee C, Theng Y. Examining COVID-19 Tweet Diffusion Using an Integrated Social Amplification of Risk and Issue-Attention Cycle Framework. Health Communication 2024;39(3):493 View
  77. Lwin M, Sheldenkar A, Lu J, Schulz P, Shin W, Panchapakesan C, Gupta R, Yang Y. The Evolution of Public Sentiments During the COVID-19 Pandemic: Case Comparisons of India, Singapore, South Korea, the United Kingdom, and the United States. JMIR Infodemiology 2022;2(1):e31473 View
  78. Jiang L, Chu T, Sun M. Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis. JMIR Infodemiology 2021;1(1):e25636 View
  79. Jones R, Mougouei D, Evans S. Understanding the emotional response to COVID‐19 information in news and social media: A mental health perspective. Human Behavior and Emerging Technologies 2021;3(5):832 View
  80. Li C, Renda M, Yusuf F, Geller J, Chun S. Public Health Policy Monitoring through Public Perceptions: A Case of COVID-19 Tweet Analysis. Information 2022;13(11):543 View
  81. Alsubaie M, Alzarah L, Alhemly F. Faculty Members’ Attitudes and Practices: How They Responded to Forced Adoption of Distance Education?. SAGE Open 2022;12(3):215824402211081 View
  82. Mourad A, Elbassuoni S. A large-scale analysis of COVID-19 tweets in the Arab region. Social Network Analysis and Mining 2022;12(1) View
  83. Mir A, Rathinam S, Gul S. Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati. Library Hi Tech 2022;40(2):340 View
  84. Huang C, Bandyopadhyay A, Fan W, Miller A, Gilbertson-White S, Chen Z. Mental toll on working women during the COVID-19 pandemic: An exploratory study using Reddit data. PLOS ONE 2023;18(1):e0280049 View
  85. Madani Y, Erritali M, Bouikhalene B. A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach. Multimedia Tools and Applications 2023;82(18):27819 View
  86. 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
  87. Alhuzali H, Zhang T, Ananiadou S. Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis. Journal of Medical Internet Research 2022;24(10):e40323 View
  88. 文 利. Analysis of Online Government Affairs Public Opinion Governance Capability. Service Science and Management 2022;11(06):237 View
  89. Hemalatha M. A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection. Expert Systems with Applications 2022;210:118227 View
  90. Alsubaie M. Distance education and the social literacy of elementary school students during the Covid-19 pandemic. Heliyon 2022;8(7):e09811 View
  91. Madni H, Umer M, Abuzinadah N, Hu Y, Saidani O, Alsubai S, Hamdi M, Ashraf I. Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model. Electronics 2023;12(6):1302 View
  92. Evans S, Jones R, Alkan E, Sichman J, Haque A, de Oliveira F, Mougouei D, Yan Z. The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing. Human Behavior and Emerging Technologies 2023;2023:1 View
  93. Muitana G, Amato C. Topics, concerns, and feelings commented on Facebook after the first death by COVID-19 in Mozambique. Revista de Investigación e Innovación en Ciencias de la Salud 2023;5(1):press View
  94. Damiano A, Xie W, Jong C. Gunning for change: A content analysis of tweets following three mass shootings in the United States. Atlantic Journal of Communication 2024;32(3):431 View
  95. Choudhary S, Sharma K, Bajaj M, Kumar A. Effectual Seed Pick Framework Focusing on Maximizing Influence in Social Networks. Wireless Communications and Mobile Computing 2023;2023:1 View
  96. Karbasi Z, Gohari S, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID‐19 based on scientific studies. Health Science Reports 2023;6(5) View
  97. Ang C, Venkatachala R. Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns. Societies 2023;13(5):117 View
  98. Li W, Haunert J, Knechtel J, Zhu J, Zhu Q, Dehbi Y. Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study. Transactions in GIS 2023;27(6):1766 View
  99. Matthews L, Schuler M, Vardavas R, Breslau J, Popescu I. Evaluation via simulation of statistical corrections for network nonindependence. Health Services and Outcomes Research Methodology 2024;24(2):211 View
  100. Luo H, Meng X, Zhao Y, Cai M. Rise of social bots: The impact of social bots on public opinion dynamics in public health emergencies from an information ecology perspective. Telematics and Informatics 2023;85:102051 View
  101. Faizah , Lin B. Visualizing Change and Correlation of Topics With LDA and Agglomerative Clustering on COVID-19 Vaccine Tweets. IEEE Access 2023;11:51647 View
  102. Kouba P, Kohout P, Haddadi F, Bushuiev A, Samusevich R, Sedlar J, Damborsky J, Pluskal T, Sivic J, Mazurenko S. Machine Learning-Guided Protein Engineering. ACS Catalysis 2023;13(21):13863 View
  103. Drescher L, Roosen J, Aue K, Dressel K, Schär W, Götz A. Emotionalität in der COVID-19-Krisenkommunikation von Behörden und unabhängigen Expert*innen auf Twitter. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 2023;66(6):689 View
  104. Ilbeigipour S, Teimourpour B. A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak. Health Services Insights 2023;16:117863292311738 View
  105. Córdoba-Cabús A, García-Borrego M, Ceballos Y. Sentiment Analysis toward the COVID-19 Vaccine in the Main Latin American Media on Twitter: The Cases of Argentina, Chile, Colombia, Mexico, and Peru. Vaccines 2023;11(10):1592 View
  106. Pinto P, Antunes M, Almeida A, Renó D. Instagram Use and Equity in Public Health: A Study on Brazil and Portugal During the COVID-19 Pandemic. Canadian Journal of Communication 2023;48(3):474 View
  107. Marqués-Sánchez P, Martínez-Fernández M, Benítez-Andrades J, Quiroga-Sánchez E, García-Ordás M, Arias-Ramos N, Limongi R. Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques. PLOS ONE 2023;18(8):e0289553 View
  108. Alturki N, Umer M, Alshardan A, Saidani O, Abate A, Ashraf I. Convolutional neural network and ensemble machine learning model for optimizing performance of emotion recognition in wild. Multimedia Tools and Applications 2023 View
  109. C. P, P. M. D. An Efficient CSPK-FCM Explainable Artificial Intelligence Model on COVID-19 Data to Predict the Emotion Using Topic Modeling. Journal of Advances in Information Technology 2023;14(6):1390 View
  110. Zou W, Li J, Yang Y, Tang L. Exploring the Early Adoption of Open AI among Laypeople and Technical Professionals: An Analysis of Twitter Conversations on #ChatGPT and #GPT3. International Journal of Human–Computer Interaction 2023:1 View
  111. Ramzy M, Ibrahim B. User satisfaction with Arabic COVID-19 apps: Sentiment analysis of users’ reviews using machine learning techniques. Information Processing & Management 2024;61(3):103644 View
  112. JIANG Y, FAHMY M, AMIN N, SOHAIL A, ALAM F, NOFAL T. ARTIFICIAL INTELLIGENCE TO DEAL WITH THE POST COVID-19 FRACTAL DYNAMICS LINKED WITH ECONOMY. Fractals 2023;31(10) View
  113. Wu X, Feng C, Li Q, Zhu J. Keyword Pool Generation for Web Text Collecting: A Framework Integrating Sample and Semantic Information. Mathematics 2024;12(3):405 View
  114. Pana B, Radu C, Furtunescu F, Mociu A, Ciufu N. Utility and Utilization of Patient-Reported Experience Measures for the Supplementary COVID-19 Protective Actions at the Ovidius Clinical Hospital in Romania. Healthcare 2024;12(3):377 View
  115. Macaluso M, Rothenberg M, Ferkol T, Kuhnell P, Kaminski H, Kimberlin D, Benatar M, Chehade M. Impact of the COVID-19 Pandemic on People Living With Rare Diseases and Their Families: Results of a National Survey. JMIR Public Health and Surveillance 2024;10:e48430 View
  116. Lin B, Zou L, Zhao B, Huang X, Cai H, Yang M, Zhou B. Sensing the pulse of the pandemic: unveiling the geographical and demographic disparities of public sentiment toward COVID-19 through social media. Cartography and Geographic Information Science 2024;51(3):366 View
  117. Cheung L, Lau A, Lam K, Ng P. A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread. COVID 2024;4(4):466 View
  118. Singh S, Dhir S, Sushil . The emotions for COVID-19 vaccine: Insights from Twitter analytics about hesitancy and willingness for vaccination. Journal of Policy Modeling 2024 View
  119. Breve B, Caruccio L, Cirillo S, Deufemia V, Polese G. Analyzing the worldwide perception of the Russia-Ukraine conflict through Twitter. Journal of Big Data 2024;11(1) View
  120. Kanilmaz U, Resch B, Holzinger R, Wasner C, Steinmaurer T. The Spatial Structures in the Austrian COVID-19 Protest Movement: A Virtual and Geospatial User Network Analysis. Social Sciences 2024;13(6):282 View
  121. Malik S, Muhammad K, Waheed Y. Artificial intelligence and industrial applications-A revolution in modern industries. Ain Shams Engineering Journal 2024:102886 View
  122. Shen K, Ding L, Kong L, Liu X. From physical space to cyberspace: Recessive gender biases in social media mirror the real world. Cities 2024;152:105149 View
  123. Shen Y, Luo Z, Song X, Liu C, Wang H. Research on the evolution of cross-platform online public opinion for public health emergencies considering stakeholders. PLOS ONE 2024;19(6):e0304877 View
  124. Olcese M, Antichi L, Madera F, Cardinali P, Prestia D, Serafini G, Dettore D, Casale S, Giannini M, Martinotti G, Migliorini L. Suicide on Italian Instagram: Insights and implications for prevention and support. Journal of Community & Applied Social Psychology 2024;34(4) View
  125. Shazley O, Wiciak M, Santhosh D. Unmasking the psychological impact of COVID-19 among young adults (ages 18-28) during the initial global lockdown: Results from a cross-sectional online survey (Preprint). JMIR Formative Research 2023 View

Books/Policy Documents

  1. Madani Y, Erritali M, Bouikhalene B. Business Intelligence. View
  2. Tan H, Lee C, Goh D, Zheng H, Theng Y. HCI International 2021 - Posters. View
  3. Kapoteli E, Chouliara V, Koukaras P, Tjortjis C. Artificial Intelligence and Machine Learning for Healthcare. View
  4. Mehanović D, Mašetić Z, Vatreš A. Advanced Technologies, Systems, and Applications VI. View
  5. Uvaneshwari M, Gupta E, Goyal M, Suman N, Geetha M. International Conference on Innovative Computing and Communications. View
  6. Ciaburro G, Iannace G, Puyana-Romero V. Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. View
  7. Soh S, Yu S, Treur J. Data Science and Intelligent Systems. View
  8. Arora A, Chakraborty P, Bhatia M. Emerging Technologies During the Era of COVID-19 Pandemic. View
  9. Pousset R. Senizid. View
  10. Sathya A, Mythili M. Advances in Artificial and Human Intelligence in the Modern Era. View
  11. Hernández-Aguilar J, Calderón-Segura Y, Medina-Angel G, Moreno-Bernal P, Bonilla-Sánchez F, del Carmen Peralta-Abarca J, Burlak G. Smart Cities. View