Published on in Vol 16, No 11 (2014): November

The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance

The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance

The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance

Journals

  1. Sharpe J, Hopkins R, Cook R, Striley C. Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis. JMIR Public Health and Surveillance 2016;2(2):e161 View
  2. Seidenberg A, Pagoto S, Vickey T, Linos E, Wehner M, Costa R, Geller A. Tanning bed burns reported on Twitter: over 15,000 in 2013. Translational Behavioral Medicine 2016;6(2):271 View
  3. Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emergency Medicine Australasia 2018;30(6):870 View
  4. Kim I, Feng C, Wang Y, Spitzberg B, Tsou M. Exploratory Spatiotemporal Analysis in Risk Communication during the MERS Outbreak in South Korea. The Professional Geographer 2017;69(4):629 View
  5. Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal 2018;35(2):91 View
  6. Schwab-Reese L, Hovdestad W, Tonmyr L, Fluke J. The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations. Child Abuse & Neglect 2018;85:187 View
  7. Djuricich A, Zee-Cheng J. Live tweeting in medicine: ‘Tweeting the meeting’. International Review of Psychiatry 2015;27(2):133 View
  8. Ukoha C, Stranieri A. Criteria to Measure Social Media Value in Health Care Settings: Narrative Literature Review. Journal of Medical Internet Research 2019;21(12):e14684 View
  9. Barnes M, Hanson C, Giraud-Carrier C. The Case for Computational Health Science. Journal of Healthcare Informatics Research 2018;2(1-2):99 View
  10. Sarker A, Magge A, Sharma A. Dermatologic concerns communicated through Twitter. International Journal of Dermatology 2017;56(8) View
  11. Gao Y, Wang S, Padmanabhan A, Yin J, Cao G. Mapping spatiotemporal patterns of events using social media: a case study of influenza trends. International Journal of Geographical Information Science 2018;32(3):425 View
  12. Rabarison K, Croston M, Englar N, Bish C, Flynn S, Johnson C. Measuring Audience Engagement for Public Health Twitter Chats: Insights From #LiveFitNOLA. JMIR Public Health and Surveillance 2017;3(2):e34 View
  13. Oldroyd R, Morris M, Birkin M. Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques. JMIR Public Health and Surveillance 2018;4(2):e57 View
  14. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  15. Cuomo R, Cai M, Shah N, Li J, Chen W, Obradovich N, Mackey T. Characterising communities impacted by the 2015 Indiana HIV outbreak: A big data analysis of social media messages associated with HIV and substance abuse. Drug and Alcohol Review 2020;39(7):908 View
  16. Hartley D, Giannini C, Wilson S, Frieder O, Margolis P, Kotagal U, White D, Connelly B, Wheeler D, Tadesse D, Macaluso M, Nishiura H. Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital. PLOS ONE 2017;12(7):e0182008 View
  17. Gao J, Zhang Y, Zhou T. Computational socioeconomics. Physics Reports 2019;817:1 View
  18. Han S, Tsou M, Clarke K, Hernandez Montoya A. Do Global Cities Enable Global Views? Using Twitter to Quantify the Level of Geographical Awareness of U.S. Cities. PLOS ONE 2015;10(7):e0132464 View
  19. Chan M, Lohmann S, Morales A, Zhai C, Ungar L, Holtgrave D, Albarracín D. An Online Risk Index for the Cross-Sectional Prediction of New HIV Chlamydia, and Gonorrhea Diagnoses Across U.S. Counties and Across Years. AIDS and Behavior 2018;22(7):2322 View
  20. Shi X, Xue B, Tsou M, Ye X, Spitzberg B, Gawron J, Corliss H, Lee J, Jin R. Detecting events from the social media through exemplar-enhanced supervised learning. International Journal of Digital Earth 2019;12(9):1083 View
  21. Zhao N, Cao G, Vanos J, Vecellio D. The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance. International Journal of Biometeorology 2018;62(1):69 View
  22. Kagashe I, Yan Z, Suheryani I. Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data. Journal of Medical Internet Research 2017;19(9):e315 View
  23. Tian E. A prospect for the geographical research of sport in the age of Big Data. Sport in Society 2020;23(1):159 View
  24. Hassan Zadeh A, Zolbanin H, Sharda R, Delen D. Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis. Information Systems Frontiers 2019;21(4):743 View
  25. Jiang W, Wang Y, Tsou M, Fu X, Amaral L. Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter). PLOS ONE 2015;10(10):e0141185 View
  26. Han S, Tsou M, Clarke K. Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space. International Journal of Digital Earth 2018;11(5):451 View
  27. Gardy J, Loman N. Towards a genomics-informed, real-time, global pathogen surveillance system. Nature Reviews Genetics 2018;19(1):9 View
  28. Tsou M. Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science 2015;42(sup1):70 View
  29. Braithwaite S, Giraud-Carrier C, West J, Barnes M, Hanson C. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality. JMIR Mental Health 2016;3(2):e21 View
  30. Ye X, Li S, Yang X, Qin C. Use of Social Media for the Detection and Analysis of Infectious Diseases in China. ISPRS International Journal of Geo-Information 2016;5(9):156 View
  31. Park H, Park S, Chong M. Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea. Journal of Medical Internet Research 2020;22(5):e18897 View
  32. Barros J, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680 View
  33. Spitzberg B. Trace of pace, place, and space in personal relationships: The chronogeometrics of studying relationships at scale. Personal Relationships 2019;26(2):184 View
  34. Allen C, Tsou M, Aslam A, Nagel A, Gawron J, Ebrahimi M. Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. PLOS ONE 2016;11(7):e0157734 View
  35. Hopkins R, Tong C, Burkom H, Akkina J, Berezowski J, Shigematsu M, Finley P, Painter I, Gamache R, Vilas V, Streichert L. A Practitioner-Driven Research Agenda for Syndromic Surveillance. Public Health Reports 2017;132(1_suppl):116S View
  36. Young S, Zhang Q, Nishiura H. Using search engine big data for predicting new HIV diagnoses. PLOS ONE 2018;13(7):e0199527 View
  37. Brownstein J, Chu S, Marathe A, Marathe M, Nguyen A, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, Tizzoni M, Vespignani A, Vullikanti A, Wilson M, Zhang Q. Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches. JMIR Public Health and Surveillance 2017;3(4):e83 View
  38. Yang J, Tsou M, Janowicz K, Clarke K, Jankowski P. Reshaping the urban hierarchy: patterns of information diffusion on social media. Geo-spatial Information Science 2019;22(3):149 View
  39. Khan Y, Leung G, Belanger P, Gournis E, Buckeridge D, Liu L, Li Y, Johnson I. Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study. Canadian Journal of Public Health 2018;109(3):419 View
  40. Yang J, Tsou M, Jung C, Allen C, Spitzberg B, Gawron J, Han S. Social media analytics and research testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages. Big Data & Society 2016;3(1):205395171665291 View
  41. Issa E, Tsou M, Nara A, Spitzberg B. Understanding the spatio-temporal characteristics of Twitter data with geotagged and non-geotagged content: two case studies with the topic of flu and Ted (movie). Annals of GIS 2017;23(3):219 View
  42. Little R, West B, Boonstra P, Hu J. Measures of the Degree of Departure from Ignorable Sample Selection. Journal of Survey Statistics and Methodology 2020;8(5):932 View
  43. Kim A, Hopper T, Simpson S, Nonnemaker J, Lieberman A, Hansen H, Guillory J, Porter L. Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study. Journal of Medical Internet Research 2015;17(11):e251 View
  44. Wilson C, Jumbert M. The new informatics of pandemic response: humanitarian technology, efficiency, and the subtle retreat of national agency. Journal of International Humanitarian Action 2018;3(1) View
  45. 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
  46. Ramirez A, Aguilar R, Merck A, Despres C, Sukumaran P, Cantu-Pawlik S, Chalela P. Use of #SaludTues Tweetchats for the Dissemination of Culturally Relevant Information on Latino Health Equity: Exploratory Case Study. JMIR Public Health and Surveillance 2021;7(3):e21266 View
  47. 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
  48. Zhao Y, He X, Feng Z, Bost S, Prosperi M, Wu Y, Guo Y, Bian J. Biases in using social media data for public health surveillance: A scoping review. International Journal of Medical Informatics 2022;164:104804 View
  49. Jabalameli S, Xu Y, Shetty S. Spatial and sentiment analysis of public opinion toward COVID-19 pandemic using twitter data: At the early stage of vaccination. International Journal of Disaster Risk Reduction 2022;80:103204 View
  50. Bartmess M, Talbot C, O’Dwyer S, Lopez R, Rose K, Anderson J. Using Twitter to understand perspectives and experiences of dementia and caregiving at the beginning of the COVID-19 pandemic. Dementia 2022;21(5):1734 View
  51. Pilipiec P, Samsten I, Bota A, Rocha L. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101 View
  52. Hammond A, Kim J, Sadler H, Vandemaele K. Influenza surveillance systems using traditional and alternative sources of data: A scoping review. Influenza and Other Respiratory Viruses 2022;16(6):965 View
  53. Cotterill S. Sport psychology practitioner's perceptions and use of social media. Asian Journal of Sport and Exercise Psychology 2022;2(3):156 View
  54. Kim I, Begay C, Ma H, Orozco F, Rogers C, Valente T, Unger J. E-Cigarette–Related Health Beliefs Expressed on Twitter Within the U.S.. AJPM Focus 2023;2(2):100067 View
  55. Rosato C, Moore R, Carter M, Heap J, Harris J, Storopoli J, Maskell S. Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models. Information 2023;14(3):170 View
  56. Osborne M, Kenah E, Lancaster K, Tien J. Catch the tweet to fight the flu: Using Twitter to promote flu shots on a college campus. Journal of American College Health 2023;71(8):2470 View
  57. Xie T, Ge Y, Xu Q, Chen S. Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance. AI 2023;4(1):333 View
  58. Jabalameli S, Xu Y, Shetty S. The Spatial and Sentiment Analysis of Public Opinion Toward Covid-19 Pandemic Using Twitter Data: At the Early Stage of Vaccination. SSRN Electronic Journal 2022 View
  59. Wang A, Dara R, Yousefinaghani S, Maier E, Sharif S. A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception. Big Data and Cognitive Computing 2023;7(2):72 View
  60. Sano Y, Hori A, Kolahi J. 12-year observation of tweets about rubella in Japan: A retrospective infodemiology study. PLOS ONE 2023;18(5):e0285101 View
  61. Boligarla S, Laison E, Li J, Mahadevan R, Ng A, Lin Y, Thioub M, Huang B, Ibrahim M, Nasri B. Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter. BMC Medical Informatics and Decision Making 2023;23(1) View
  62. Deiner M, Deiner N, Hristidis V, McLeod S, Doan T, Lietman T, Porco T. of large language models to assess likelihood of epidemics from content of Tweets: Infodemiology Study (Preprint). Journal of Medical Internet Research 2023 View

Books/Policy Documents

  1. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. View
  2. Ye X, Li S, Yang X, Lee J, Wu L. Big Data Support of Urban Planning and Management. View
  3. Nayduch D, Fryxell R, Olafson P. Medical and Veterinary Entomology. View
  4. Morris N. Routledge Handbook of Health Geography. View
  5. Fan S, Garg S, Yeom S. AI 2016: Advances in Artificial Intelligence. View
  6. Spitzberg B, Tsou M, Jung C. The Handbook of Applied Communication Research. View
  7. Martinez L, Tsou M, Spitzberg B. Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics. View