Published on in Vol 6, No 2 (2020): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19509, first published .
Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Journals

  1. Brown S, Rhee J, Guha A, Rao V. Innovation in Precision Cardio-Oncology During the Coronavirus Pandemic and Into a Post-pandemic World. Frontiers in Cardiovascular Medicine 2020;7 View
  2. Picone M, Inoue S, DeFelice C, Naujokas M, Sinrod J, Cruz V, Stapleton J, Sinrod E, Diebel S, Wassman E. Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals. Population Health Management 2020;23(5):350 View
  3. Xue J, Chen J, Chen C, Zheng C, Li S, Zhu T, Zhao J. Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLOS ONE 2020;15(9):e0239441 View
  4. Al-Rawi A, Shukla V. Bots as Active News Promoters: A Digital Analysis of COVID-19 Tweets. Information 2020;11(10):461 View
  5. Haupt M, Jinich-Diamant A, Li J, Nali M, Mackey T. Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis. Online Social Networks and Media 2021;21:100114 View
  6. 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
  7. Kumar S, Xu C, Ghildayal N, Chandra C, Yang M. Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic. Annals of Operations Research 2022;319(1):823 View
  8. Tayarani N. M. Applications of artificial intelligence in battling against covid-19: A literature review. Chaos, Solitons & Fractals 2021;142:110338 View
  9. Klein A, Magge A, O'Connor K, Flores Amaro J, Weissenbacher D, Gonzalez Hernandez G. Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set. Journal of Medical Internet Research 2021;23(1):e25314 View
  10. Poulin R, Bennett J, Filion A, Bhattarai U, Chai X, de Angeli Dutra D, Donlon E, Doherty J, Jorge F, Milotic M, Park E, Sabadel A, Thomas L. iParasitology: Mining the Internet to Test Parasitological Hypotheses. Trends in Parasitology 2021;37(4):267 View
  11. Xue J, Chen J, Hu R, Chen C, Zheng C, Su Y, Zhu T. Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. Journal of Medical Internet Research 2020;22(11):e20550 View
  12. Alomari E, Katib I, Albeshri A, Mehmood R. COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning. International Journal of Environmental Research and Public Health 2021;18(1):282 View
  13. Saha K, Torous J, Caine E, De Choudhury M. Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media. Journal of Medical Internet Research 2020;22(11):e22600 View
  14. Alanazi E, Alashaikh A, Alqurashi S, Alanazi A. Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis. Journal of Medical Internet Research 2020;22(11):e21329 View
  15. Ayumi V. Application of Machine Learning for SARS-CoV-2 Outbreak. International Journal of Scientific Research in Science, Engineering and Technology 2021:241 View
  16. 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
  17. Do Q, Marc D, Plotkin M, Pickering B, Herasevich V. Starter Kit for Geotagging and Geovisualization in Health Care: Resource Paper. JMIR Formative Research 2020;4(12):e23379 View
  18. Garcia K, Berton L. Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing 2021;101:107057 View
  19. Celuppi I, Lima G, Rossi E, Wazlawick R, Dalmarco E. Uma análise sobre o desenvolvimento de tecnologias digitais em saúde para o enfrentamento da COVID-19 no Brasil e no mundo. Cadernos de Saúde Pública 2021;37(3) View
  20. Peterson K, Lewis J, Patterson O, Chapman A, Denhalter D, Lye P, Stevens V, Gamage S, Roselle G, Wallace K, Jones M. Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation. JMIR Public Health and Surveillance 2021;7(3):e26719 View
  21. Fiok K, Karwowski W, Gutierrez E, Saeidi M, Aljuaid A, Davahli M, Taiar R, Marek T, Sawyer B. A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter® in the United States: A Natural Language Processing Approach. International Journal of Environmental Research and Public Health 2021;18(9):4543 View
  22. Sooknanan J, Mays N. Harnessing Social Media in the Modelling of Pandemics—Challenges and Opportunities. Bulletin of Mathematical Biology 2021;83(5) View
  23. Dixon B, Mukherjee S, Wiensch A, Gray M, Ferres J, Grannis S. Capturing COVID-19–Like Symptoms at Scale Using Banner Ads on an Online News Platform: Pilot Survey Study. Journal of Medical Internet Research 2021;23(5):e24742 View
  24. 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
  25. Zhao J, Grabowska M, Kerchberger V, Smith J, Eken H, Feng Q, Peterson J, Trent Rosenbloom S, Johnson K, Wei W. ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes. Journal of Biomedical Informatics 2021;117:103748 View
  26. 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
  27. Goodswen S, Barratt J, Kennedy P, Kaufer A, Calarco L, Ellis J. Machine learning and applications in microbiology. FEMS Microbiology Reviews 2021;45(5) View
  28. Shahid O, Nasajpour M, Pouriyeh S, Parizi R, Han M, Valero M, Li F, Aledhari M, Sheng Q. Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. Journal of Biomedical Informatics 2021;117:103751 View
  29. Massey D, Huang C, Lu Y, Cohen A, Oren Y, Moed T, Matzner P, Mahajan S, Caraballo C, Kumar N, Xue Y, Ding Q, Dreyer R, Roy B, Krumholz H. Engagement With COVID-19 Public Health Measures in the United States: A Cross-sectional Social Media Analysis from June to November 2020. Journal of Medical Internet Research 2021;23(6):e26655 View
  30. 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
  31. Yousefinaghani S, Dara R, Mubareka S, Sharif S. Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada. Frontiers in Public Health 2021;9 View
  32. 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
  33. Haupt M, Li J, Mackey T. Identifying and characterizing scientific authority-related misinformation discourse about hydroxychloroquine on twitter using unsupervised machine learning. Big Data & Society 2021;8(1):205395172110138 View
  34. Ilyas H, Anwar A, Yaqub U, Alzamil Z, Appelbaum D. Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India. Global Knowledge, Memory and Communication 2022;71(3):140 View
  35. Elgazzar H, Spurlock K, Bogart T. Evolutionary clustering and community detection algorithms for social media health surveillance. Machine Learning with Applications 2021;6:100084 View
  36. Al-Rawi A, Grepin K, Li X, Morgan R, Wenham C, Smith J. Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data. Journal of Healthcare Informatics Research 2021;5(3):249 View
  37. Miliou I, Xiong X, Rinzivillo S, Zhang Q, Rossetti G, Giannotti F, Pedreschi D, Vespignani A, Viboud C. Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology 2021;17(7):e1009087 View
  38. Riswantini D, Nugraheni E, Arisal A, Khotimah P, Munandar D, Suwarningsih W. Big Data Research in Fighting COVID-19: Contributions and Techniques. Big Data and Cognitive Computing 2021;5(3):30 View
  39. 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
  40. Rabiolo A, Alladio E, Morales E, McNaught A, Bandello F, Afifi A, Marchese A. Forecasting the COVID-19 Epidemic By Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study. Journal of Medical Internet Research 2021;23(8):e28876 View
  41. Zhang Y, Zhang Q, Zhao Y, Deng Y, Zheng H. Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic. International Journal of Applied Earth Observation and Geoinformation 2022;112:102942 View
  42. Golder S, Klein A, Magge A, O’Connor K, Cai H, Weissenbacher D, Gonzalez-Hernandez G. A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK. DIGITAL HEALTH 2022;8:205520762210975 View
  43. Ogie R, James S, Moore A, Dilworth T, Amirghasemi M, Whittaker J. Social media use in disaster recovery: A systematic literature review. International Journal of Disaster Risk Reduction 2022;70:102783 View
  44. Ling-Hu T, Rios-Guzman E, Lorenzo-Redondo R, Ozer E, Hultquist J. Challenges and Opportunities for Global Genomic Surveillance Strategies in the COVID-19 Era. Viruses 2022;14(11):2532 View
  45. Cuomo R, Purushothaman V, Calac A, McMann T, Li Z, Mackey T. Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study. JMIR Formative Research 2023;7:e42162 View
  46. Devkota J, Haughton J. Estimation of Underreported Cases of Infections and Deaths from COVID-19 for Countries with Limited and Scarce Data: Examples from Nepal. Journal of Environmental and Public Health 2022;2022:1 View
  47. Karatas M, Eriskin L, Deveci M, Pamucar D, Garg H. Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications 2022;200:116912 View
  48. Lopez C, Gallemore C. An augmented multilingual Twitter dataset for studying the COVID-19 infodemic. Social Network Analysis and Mining 2021;11(1) View
  49. Ghanem A, Asaad C, Hafidi H, Moukafih Y, Guermah B, Sbihi N, Zakroum M, Ghogho M, Dairi M, Cherqaoui M, Baina K. Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management. International Journal of Environmental Research and Public Health 2021;18(22):12172 View
  50. Ng R, Indran N, Liu L. Ageism on Twitter during the COVID‐19 pandemic. Journal of Social Issues 2022;78(4):842 View
  51. Jafarzadeh H, Pauleen D, Abedin E, Weerasinghe K, Taskin N, Coskun M, Mehmood R. Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter. PLOS ONE 2021;16(12):e0259882 View
  52. Renner S, Loussikian P, Foulquié P, Arnould B, Marrel A, Barbier V, Mebarki A, Schück S, Bharmal M. Perceived Unmet Needs in Patients Living With Advanced Bladder Cancer and Their Caregivers: Infodemiology Study Using Data From Social Media in the United States. JMIR Cancer 2022;8(3):e37518 View
  53. Kulawiak M, Kulawiak N, Sulima M, Sikorska K. A novel architecture of Web-GIS for mapping and analysis of echinococcosis in Poland. Applied Geomatics 2022;14(2):181 View
  54. Luo X, Gandhi P, Storey S, Huang K. A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media. IEEE Journal of Biomedical and Health Informatics 2022;26(4):1737 View
  55. Sun H, Zhang Y, Gao G, Wu D. Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives. Frontiers in Public Health 2022;10 View
  56. Ilbeigipour S, Albadvi A, Akhondzadeh Noughabi E. Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making. Informatics in Medicine Unlocked 2022;32:101005 View
  57. KAYA A, GÜMÜŞ R, AYDIN Ö. TIME SERIES OUTLIER ANALYSIS FOR MODEL, DATA AND HUMAN-INDUCED RISKS IN COVID-19 SYMPTOMS DETECTION. Middle East Journal of Science 2021;7(2):123 View
  58. de Carvalho V, Nepomuceno T, Poleto T, Costa A. The COVID-19 Infodemic on Twitter: A Space and Time Topic Analysis of the Brazilian Immunization Program and Public Trust. Tropical Medicine and Infectious Disease 2022;7(12):425 View
  59. Malik A, Berggren W, Al-Busaidi A. Instagram as a research tool for examining tobacco-related content: A methodological review. Technology in Society 2022;70:102008 View
  60. Mahdikhani M. Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic. International Journal of Information Management Data Insights 2022;2(1):100053 View
  61. Hernandez L, Callahan T, Banda J. A biomedically oriented automatically annotated Twitter COVID-19 dataset. Genomics & Informatics 2021;19(3):e21 View
  62. Usher K, Durkin J, Martin S, Vanderslott S, Vindrola-Padros C, Usher L, Jackson D. Public Sentiment and Discourse on Domestic Violence During the COVID-19 Pandemic in Australia: Analysis of Social Media Posts. Journal of Medical Internet Research 2021;23(10):e29025 View
  63. Thakur N, Han C. An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection. COVID 2022;2(8):1026 View
  64. Saniei R, Rodríguez Doncel V. PHDD: Corpus of Physical Health Data Disclosure on Twitter During COVID-19 Pandemic. SN Computer Science 2022;3(3) View
  65. Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. International Journal of Applied Earth Observation and Geoinformation 2022;113:102967 View
  66. Daradkeh M. Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media. International Journal of Business Analytics 2021;9(3):1 View
  67. Luo L, Wang Y, Mo D. Identifying COVID-19 Personal Health Mentions From Tweets Using Masked Attention Model. IEEE Access 2022;10:59068 View
  68. Didi Y, Walha A, Wali A. COVID-19 Tweets Classification Based on a Hybrid Word Embedding Method. Big Data and Cognitive Computing 2022;6(2):58 View
  69. Honcharov V, Li J, Sierra M, Rivadeneira N, Olazo K, Nguyen T, Mackey T, Sarkar U. Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis. JMIR Infodemiology 2023;3:e40575 View
  70. Moreno-Ortiz A, García-Gámez M. Strategies for the Analysis of Large Social Media Corpora: Sampling and Keyword Extraction Methods. Corpus Pragmatics 2023;7(3):241 View
  71. Klein A, Kunatharaju S, O'Connor K, Gonzalez-Hernandez G. Automatically Identifying Self-Reports of COVID-19 Diagnosis on Twitter: An Annotated Data Set, Deep Neural Network Classifiers, and a Large-Scale Cohort. Journal of Medical Internet Research 2023;25:e46484 View
  72. Dolatabadi E, Moyano D, Bales M, Spasojevic S, Bhambhoria R, Bhatti J, Debnath S, Hoell N, Li X, Leng C, Nanda S, Saab J, Sahak E, Sie F, Uppal S, Vadlamudi N, Vladimirova A, Yakimovich A, Yang X, Kocak S, Cheung A. Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach. Journal of Medical Internet Research 2023;25:e45767 View
  73. Tian Y, Zhang W, Duan L, McDonald W, Osgood N. Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Frontiers in Digital Health 2023;5 View
  74. Long K, Kwok S, Kotz J, Wang G. A deep multi-view imbalanced learning approach for identifying informative COVID-19 tweets from social media. Computers in Biology and Medicine 2023;164:107232 View
  75. Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski L, Mikkilineni R, Rushmore R, Wagner T. The evolution of Big Data in neuroscience and neurology. Journal of Big Data 2023;10(1) View
  76. Thakur N. Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis. Applied System Innovation 2023;6(5):92 View
  77. Nali M, McMann T, Purushothaman V, Li Z, Cuomo R, Liang B, Mackey T. Assessing Characteristics and Compliance of Online Delta-8 Tetrahydrocannabinol Product Sellers. Cannabis and Cannabinoid Research 2023 View
  78. Kaur M, Cargill T, Hui K, Vu M, Bragazzi N, Kong J. A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets. JMIR Formative Research 2024;8:e46087 View
  79. McMann T, Wenzel C, Le N, Li Z, Xu Q, Cuomo R, Mackey T. Detection and Characterization of Web-Based Pediatric COVID-19 Vaccine Discussions and Racial and Ethnic Minority Topics: Retrospective Analysis of Twitter Data. JMIR Pediatrics and Parenting 2023;6:e48004 View
  80. 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
  81. Haupt M, Chiu M, Chang J, Li Z, Cuomo R, Mackey T, Cresci S. Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding. PLOS ONE 2023;18(12):e0295414 View

Books/Policy Documents

  1. Alexiou E, Antonakakis A, Jevtic N, Sideras G, Farmaki E, Foutsitzi S, Kermanidis K. Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops. View
  2. Misra P, Yadav A, Chaurasia S. New Opportunities for Sentiment Analysis and Information Processing. View
  3. . Applied Big Data Analytics and Its Role in COVID-19 Research. View
  4. Ghosal A, Gupta N, Nandi E, Somolu H. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. View
  5. Bhambhoria R, Saab J, Uppal S, Li X, Yakimovich A, Bhatti J, Valdamudi N, Moyano D, Bales M, Dolatabadi E, Kocak S. Multimodal AI in Healthcare. View
  6. Goswami M, Sebastian N. Innovative Data Communication Technologies and Application. View
  7. Ferdib-Al-Islam , Ghosh M. Proceedings of the International Conference on Big Data, IoT, and Machine Learning. View
  8. Narawade V, Dandekar A. Soft Computing and Signal Processing. View
  9. Dixon B, Barros Sierra Cordera D, Hernández Ávila M, Wang X, Zhang L, Romero W, Zepeda Tello R. Modernizing Global Health Security to Prevent, Detect, and Respond. View