Published on in Vol 17, No 6 (2015): June

A Scalable Framework to Detect Personal Health Mentions on Twitter

A Scalable Framework to Detect Personal Health Mentions on Twitter

A Scalable Framework to Detect Personal Health Mentions on Twitter

Journals

  1. Rashidi T, Abbasi A, Maghrebi M, Hasan S, Waller T. Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges. Transportation Research Part C: Emerging Technologies 2017;75:197 View
  2. 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
  3. Wakamiya S, Matsune S, Okubo K, Aramaki E. Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis. Journal of Medical Internet Research 2019;21(2):e10450 View
  4. Yin Z, Sulieman L, Malin B. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561 View
  5. 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
  6. Nguyen Q, Li D, Meng H, Kath S, Nsoesie E, Li F, Wen M. Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity. JMIR Public Health and Surveillance 2016;2(2):e158 View
  7. 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
  8. Cole D, Nick E, Varga G, Smith D, Zelkowitz R, Ford M, Lédeczi Á. Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support. Cyberpsychology, Behavior, and Social Networking 2019;22(11):692 View
  9. Nguyen T, Meng H, Sandeep S, McCullough M, Yu W, Lau Y, Huang D, Nguyen Q. Twitter-derived measures of sentiment towards minorities (2015–2016) and associations with low birth weight and preterm birth in the United States. Computers in Human Behavior 2018;89:308 View
  10. Srivastava S, Singh S, Suri J. Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm. Computer Methods and Programs in Biomedicine 2019;172:35 View
  11. Yin Z, Harrell M, Warner J, Chen Q, Fabbri D, Malin B. The therapy is making me sick: how online portal communications between breast cancer patients and physicians indicate medication discontinuation. Journal of the American Medical Informatics Association 2018;25(11):1444 View
  12. Yoon S. What Can We Learn About Mental Health Needs From Tweets Mentioning Dementia on World Alzheimer’s Day?. Journal of the American Psychiatric Nurses Association 2016;22(6):498 View
  13. Nguyen Q, Brunisholz K, Yu W, McCullough M, Hanson H, Litchman M, Li F, Wan Y, VanDerslice J, Wen M, Smith K. Twitter-derived neighborhood characteristics associated with obesity and diabetes. Scientific Reports 2017;7(1) View
  14. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  15. Haghighi N, Liu X, Wei R, Li W, Shao H. Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service. Public Transport 2018;10(2):363 View
  16. B.S. V, Wong R, Chi C. Health Mentions on Twitter: A Case Study to Identify Privacy Leaks. IEEE Consumer Electronics Magazine 2020;9(5):85 View
  17. Xu S, Markson C, Costello K, Xing C, Demissie K, Llanos A. Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter. JMIR Public Health and Surveillance 2016;2(1):e17 View
  18. Liu Y, Yin Z. Understanding Weight Loss via Online Discussions: Content Analysis of Reddit Posts Using Topic Modeling and Word Clustering Techniques. Journal of Medical Internet Research 2020;22(6):e13745 View
  19. Chisholm E, O’Sullivan K. Using Twitter to Explore (un)Healthy Housing: Learning from the #Characterbuildings Campaign in New Zealand. International Journal of Environmental Research and Public Health 2017;14(11):1424 View
  20. Alvaro N, Miyao Y, Collier N. TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations. JMIR Public Health and Surveillance 2017;3(2):e24 View
  21. Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal 2018;35(2):91 View
  22. Créquit P, Mansouri G, Benchoufi M, Vivot A, Ravaud P. Mapping of Crowdsourcing in Health: Systematic Review. Journal of Medical Internet Research 2018;20(5):e187 View
  23. Carter A, Hoy M, DeSimone B. Social media engagement tactics in U.S. community policing: Potential privacy and security concerns. The Police Journal: Theory, Practice and Principles 2021;94(4):556 View
  24. Mutanga M, Abayomi A. Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach. African Journal of Science, Technology, Innovation and Development 2022;14(1):163 View
  25. Alam M, Sadri A, Jin X. Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions. Future Transportation 2021;1(3):794 View
  26. Alam M, Sadri A. Examining the Communication Pattern of Transportation and Transit Agencies on Twitter: A Longitudinal Study in the Emergence of COVID-19 on Twitter. Transportation Research Record: Journal of the Transportation Research Board 2022:036119812210825 View
  27. Shakeri Hossein Abad Z, Butler G, Thompson W, Lee J. Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk. Journal of Medical Internet Research 2022;24(1):e28749 View
  28. Khademi Habibabadi S, Palmer C, Dimaguila G, Javed M, Clothier H, Buttery J. Australasian Institute of Digital Health Summit 2022–Automated Social Media Surveillance for Detection of Vaccine Safety Signals: A Validation Study. Applied Clinical Informatics 2023;14(01):01 View
  29. Stemmer M, Parmet Y, Ravid G. Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study. Journal of Medical Internet Research 2022;24(8):e29186 View
  30. Luo L, Wang Y, Mo D. Identifying COVID-19 Personal Health Mentions From Tweets Using Masked Attention Model. IEEE Access 2022;10:59068 View
  31. Di Cara N, Maggio V, Davis O, Haworth C. Methodologies for Monitoring Mental Health on Twitter: Systematic Review. Journal of Medical Internet Research 2023;25:e42734 View
  32. Suzuki N, Takumi Y. A Study of the Trends of Pollen Dispersal and Hay Fever Symptoms Using Twitter. Nippon Jibiinkoka Tokeibugeka Gakkai Kaiho(Tokyo) 2023;126(6):777 View
  33. Stemmer M, Ravid G, Parmet Y. Natural Language Processing for Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience. Procedia Computer Science 2024;237:811 View

Books/Policy Documents

  1. Vidyalakshmi B, Wong R. Information and Communications Security. View
  2. Yin Z, Warner J, Song L, Hsueh P, Chen C, Malin B. Social Web and Health Research. View
  3. Amrani G, Khennou F, Chaoui N. Information and Software Technologies. View
  4. Spitzberg B, Tsou M, Gawron M. Communicating Science in Times of Crisis. View
  5. Stemmer M, Parmet Y, Ravid G. ICT for Health, Accessibility and Wellbeing. View
  6. Yin Z, Ni C, Fabbri D, Rosenbloom S, Malin B. Personal Health Informatics. View