Published on in Vol 15, No 10 (2013): October

The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets

The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets

The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets

Journals

  1. Nagar R, Yuan Q, Freifeld C, Santillana M, Nojima A, Chunara R, Brownstein J. A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives. Journal of Medical Internet Research 2014;16(10):e236 View
  2. 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
  3. Wang Y, Fu X, Jiang W, Wang T, Tsou M, Ye X. Inferring urban air quality based on social media. Computers, Environment and Urban Systems 2017;66:110 View
  4. Wang Y, Wang T, Ye X, Zhu J, Lee J. Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm. Sustainability 2015;8(1):25 View
  5. 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
  6. 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
  7. Gurajala S, Dhaniyala S, Matthews J. Understanding Public Response to Air Quality Using Tweet Analysis. Social Media + Society 2019;5(3):205630511986765 View
  8. Yan S, Chughtai A, Macintyre C. Utility and potential of rapid epidemic intelligence from internet-based sources. International Journal of Infectious Diseases 2017;63:77 View
  9. SPRECO A, ERIKSSON O, DAHLSTRÖM Ö, TIMPKA T. Influenza detection and prediction algorithms: comparative accuracy trial in Östergötland county, Sweden, 2008–2012. Epidemiology and Infection 2017;145(10):2166 View
  10. Yin Z, Fabbri D, Rosenbloom S, Malin B. A Scalable Framework to Detect Personal Health Mentions on Twitter. Journal of Medical Internet Research 2015;17(6):e138 View
  11. 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
  12. Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal 2018;35(2):91 View
  13. Generous N, Fairchild G, Deshpande A, Del Valle S, Priedhorsky R, Salathé M. Global Disease Monitoring and Forecasting with Wikipedia. PLoS Computational Biology 2014;10(11):e1003892 View
  14. Lardon J, Bellet F, Aboukhamis R, Asfari H, Souvignet J, Jaulent M, Beyens M, Lillo-LeLouët A, Bousquet C. Evaluating Twitter as a complementary data source for pharmacovigilance. Expert Opinion on Drug Safety 2018;17(8):763 View
  15. Zhang Y, Bambrick H, Mengersen K, Tong S, Feng L, Zhang L, Liu G, Xu A, Hu W. Using big data to predict pertussis infections in Jinan city, China: a time series analysis. International Journal of Biometeorology 2020;64(1):95 View
  16. Broniatowski D, Dredze M, Paul M, Dugas A. Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study. JMIR Public Health and Surveillance 2015;1(1):e5 View
  17. 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
  18. Nguyen Q, McCullough M, Meng H, Paul D, Li D, Kath S, Loomis G, Nsoesie E, Wen M, Smith K, Li F. Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015–2016. American Journal of Public Health 2017;107(11):1776 View
  19. 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
  20. Tulloch J, Vivancos R, Christley R, Radford A, Warner J. Mapping tweets to a known disease epidemiology; a case study of Lyme disease in the United Kingdom and Republic of Ireland. Journal of Biomedical Informatics: X 2019;4:100060 View
  21. Finfgeld-Connett D. Twitter and Health Science Research. Western Journal of Nursing Research 2015;37(10):1269 View
  22. 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
  23. Aslam A, Tsou M, Spitzberg B, An L, Gawron J, Gupta D, Peddecord K, Nagel A, Allen C, Yang J, Lindsay S. The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance. Journal of Medical Internet Research 2014;16(11):e250 View
  24. Padrez K, Ungar L, Schwartz H, Smith R, Hill S, Antanavicius T, Brown D, Crutchley P, Asch D, Merchant R. Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department. BMJ Quality & Safety 2016;25(6):414 View
  25. 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
  26. 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
  27. Cao G, Wang S, Hwang M, Padmanabhan A, Zhang Z, Soltani K. A scalable framework for spatiotemporal analysis of location-based social media data. Computers, Environment and Urban Systems 2015;51:70 View
  28. Zhang A, Albrecht L, Scott S. Using Twitter for Data Collection With Health-Care Consumers. International Journal of Qualitative Methods 2018;17(1):160940691775078 View
  29. Spitzberg B. Toward A Model of Meme Diffusion (M3D). Communication Theory 2014;24(3):311 View
  30. An L, Tsou M, Spitzberg B, Gupta D, Gawron J. Latent trajectory models for space-time analysis: An application in deciphering spatial panel data. Geographical Analysis 2016;48(3):314 View
  31. 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
  32. 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
  33. Tian E. A prospect for the geographical research of sport in the age of Big Data. Sport in Society 2020;23(1):159 View
  34. 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
  35. 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
  36. 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
  37. Radin J, Wineinger N, Topol E, Steinhubl S. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. The Lancet Digital Health 2020;2(2):e85 View
  38. Zhang Y, Bambrick H, Mengersen K, Tong S, Hu W. Using Google Trends and ambient temperature to predict seasonal influenza outbreaks. Environment International 2018;117:284 View
  39. 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
  40. Kim K, Kojima I, Ogawa H. Discovery of local topics by using latent spatio-temporal relationships in geo-social media. International Journal of Geographical Information Science 2016;30(9):1899 View
  41. 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
  42. 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

Books/Policy Documents

  1. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. View
  2. Spitzberg B, Tsou M, Jung C. The Handbook of Applied Communication Research. View
  3. Nara A, Tsou M, Yang J, Huang C. Human Dynamics Research in Smart and Connected Communities. View
  4. Xiong Y, He Y, Huang H, Yu C, Jing X. Signal and Information Processing, Networking and Computers. View
  5. Yang J, Tsou M, Spitzberg B, An L, Gawron J, Gupta D. CyberGIS for Geospatial Discovery and Innovation. View
  6. Spitzberg B, Tsou M, Gawron M. Communicating Science in Times of Crisis. View