This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1365) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1049) Insomnia and Sleep Hygiene (174)

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

Characterizing Sleep Issues Using Twitter

Characterizing Sleep Issues Using Twitter

Characterizing Sleep Issues Using Twitter

Authors of this article:

David J McIver1 Author Orcid Image ;   Jared B Hawkins1 Author Orcid Image ;   Rumi Chunara1, 2 Author Orcid Image ;   Arnaub K Chatterjee3 Author Orcid Image ;   Aman Bhandari3 Author Orcid Image ;   Timothy P Fitzgerald4 Author Orcid Image ;   Sachin H Jain5 Author Orcid Image ;   John S Brownstein1 Author Orcid Image
Article Authors Cited by (59) Tweetations (79) Metrics

Journals

  1. 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
  2. Burke-Garcia A, Stanton C. A tale of two tools: Reliability and feasibility of social media measurement tools examining e-cigarette twitter mentions. Informatics in Medicine Unlocked 2017;8:8 View
  3. Hswen Y, Naslund J, Brownstein J, Hawkins J. Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study. JMIR Mental Health 2018;5(4):e11483 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. Katsuki T, Mackey T, Cuomo R. Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data. Journal of Medical Internet Research 2015;17(12):e280 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. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  8. Doan S, Yang E, Tilak S, Li P, Zisook D, Torii M. Extracting health-related causality from twitter messages using natural language processing. BMC Medical Informatics and Decision Making 2019;19(S3) View
  9. 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
  10. Piña-García C, Siqueiros-García J, Robles-Belmont E, Carreón G, Gershenson C, López J. From neuroscience to computer science: a topical approach on Twitter. Journal of Computational Social Science 2018;1(1):187 View
  11. Jaidka K, Giorgi S, Schwartz H, Kern M, Ungar L, Eichstaedt J. Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods. Proceedings of the National Academy of Sciences 2020;117(19):10165 View
  12. Piña-García C, Ramírez-Ramírez L. Exploring crime patterns in Mexico City. Journal of Big Data 2019;6(1) View
  13. Hswen Y, Qin Q, Brownstein J, Hawkins J. Feasibility of using social media to monitor outdoor air pollution in London, England. Preventive Medicine 2019;121:86 View
  14. Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S. Sleep Quality Prediction From Wearable Data Using Deep Learning. JMIR mHealth and uHealth 2016;4(4):e125 View
  15. Albalawi Y, Sixsmith J. Agenda Setting for Health Promotion: Exploring an Adapted Model for the Social Media Era. JMIR Public Health and Surveillance 2015;1(2):e21 View
  16. Gibbons J, Malouf R, Spitzberg B, Martinez L, Appleyard B, Thompson C, Nara A, Tsou M, Danforth C. Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE 2019;14(7):e0219550 View
  17. Kunkle S, Christie G, Yach D, El-Sayed A. The Importance of Computer Science for Public Health Training: An Opportunity and Call to Action. JMIR Public Health and Surveillance 2016;2(1):e10 View
  18. Hswen Y, Naslund J, Chandrashekar P, Siegel R, Brownstein J, Hawkins J. Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry Research 2017;257:479 View
  19. Huang D, Huang Y, Khanna S, Dwivedi P, Slopen N, Green K, He X, Puett R, Nguyen Q. Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample. JMIR Public Health and Surveillance 2020;6(3):e17969 View
  20. 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
  21. Hswen Y, Naslund J, Brownstein J, Hawkins J. Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media. Psychiatric Quarterly 2018;89(3):569 View
  22. Tian X, Batterham P, Song S, Yao X, Yu G. Characterizing Depression Issues on Sina Weibo. International Journal of Environmental Research and Public Health 2018;15(4):764 View
  23. Powell G, Seifert H, Reblin T, Burstein P, Blowers J, Menius J, Painter J, Thomas M, Pierce C, Rodriguez H, Brownstein J, Freifeld C, Bell H, Dasgupta N. Social Media Listening for Routine Post-Marketing Safety Surveillance. Drug Safety 2016;39(5):443 View
  24. Hausmann J, Touloumtzis C, White M, Colbert J, Gooding H. Adolescent and Young Adult Use of Social Media for Health and Its Implications. Journal of Adolescent Health 2017;60(6):714 View
  25. Anwar M, Khoury D, Aldridge A, Parker S, Conway K. Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study. JMIR Public Health and Surveillance 2020;6(2):e17574 View
  26. Anýž J, Bakštein E, Dudysová D, Veldová K, Kliková M, Fárková E, Kopřivová J, Španiel F. No wink of sleep: Population sleep characteristics in response to the brexit poll and the 2016 U.S. presidential election. Social Science & Medicine 2019;222:112 View
  27. GÜNGÖR S. Türkiye’de Hastanelerin Instagram Kullanımı: Medical Park, Acıbadem ve Memorial Sağlık Grupları Örneği. Erciyes İletişim Dergisi 2019;6(2):1309 View
  28. Hawkins A, Filtness A. Driver sleepiness on YouTube: A content analysis. Accident Analysis & Prevention 2017;99:459 View
  29. 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
  30. Hswen Y, Gopaluni A, Brownstein J, Hawkins J. Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study. JMIR mHealth and uHealth 2019;7(2):e12264 View
  31. Nguyen T, Larsen M, O’Dea B, Nguyen H, Nguyen D, Yearwood J, Phung D, Venkatesh S, Christensen H. Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices. Future Generation Computer Systems 2020;110:620 View
  32. Bailey S, Zhang Y, Ramesh A, Golbeck J, Getoor L. A Structured and Linguistic Approach to Understanding Recovery and Relapse in AA. ACM Transactions on the Web 2021;15(1):1 View
  33. Grande D, Luna Marti X, Merchant R, Asch D, Dolan A, Sharma M, Cannuscio C. Consumer Views on Health Applications of Consumer Digital Data and Health Privacy Among US Adults: Qualitative Interview Study. Journal of Medical Internet Research 2021;23(6):e29395 View
  34. Thorpe Huerta D, Hawkins J, Brownstein J, Hswen Y. Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis. SSM - Population Health 2021;15:100851 View
  35. Sakib A, Mukta M, Huda F, Islam A, Islam T, Ali M. Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets. Journal of Medical Internet Research 2021;23(12):e27613 View
  36. Lin B, Zou L, Duffield N, Mostafavi A, Cai H, Zhou B, Tao J, Yang M, Mandal D, Abedin J. Revealing the linguistic and geographical disparities of public awareness to Covid-19 outbreak through social media. International Journal of Digital Earth 2022;15(1):868 View
  37. Cohen Zion M, Gescheit I, Levy N, Yom-Tov E. Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire. Journal of Medical Internet Research 2022;24(11):e41288 View
  38. Lee I, Juang S, Chen S, Ko C, Ma K. Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience. Frontiers in Medicine 2022;9 View
  39. Meyerson W, Fineberg S, Song Y, Faber A, Ash G, Andrade F, Corlett P, Gerstein M, Hoyle R. Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys. JMIR Formative Research 2023;7:e38112 View
  40. 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
  41. Halkos G, Managi S. New developments in the disciplines of environmental and resource economics. Economic Analysis and Policy 2023;77:513 View
  42. Ladis I, Valladares T, Coppersmith D, Glenn J, Nobles A, Barnes L, Teachman B. Inferring sleep disturbance from text messages of suicide attempt survivors: A pilot study. Suicide and Life-Threatening Behavior 2023;53(1):39 View
  43. Linnell K, Arnold M, Alshaabi T, McAndrew T, Lim J, Dodds P, Danforth C. The sleep loss insult of Spring Daylight Savings in the US is observable in Twitter activity. Journal of Big Data 2021;8(1) View
  44. Sachini E, Sioumalas-⁠ Christodoulou K, Bouras N, Karampekios N. Lessons for science and technology policy? Probing the Linkedin network of an RDI organisation. SN Social Sciences 2022;2(12) View
  45. Unnikrishnan R, S. S, V.S. A. Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical data. Engineering Applications of Artificial Intelligence 2023;123:106214 View
  46. Duan J, Li Q, Yin Z, Zhen S, Cao W, Yan S, Zhang Y, Wu Q, Zhang W, Liang F. Outdoor Artificial Light at Night and Insomnia-Related Social Media Posts. JAMA Network Open 2024;7(11):e2446156 View

Books/Policy Documents

  1. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Social Media and the Armed Forces. View
  2. López J, Piña-García C. Complex Networks & Their Applications V. View
  3. Amador J, Piña-Garcia C. Online Communities as Agents of Change and Social Movements. View
  4. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Soziale Medien und die Streitkräfte. View