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

Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 17, No 4 (2015): April

The Painful Tweet: Text, Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain

The Painful Tweet: Text, Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain

The Painful Tweet: Text, Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain

Authors of this article:

Patrick J Tighe1 Author Orcid Image ;   Ryan C Goldsmith1 Author Orcid Image ;   Michael Gravenstein1 Author Orcid Image ;   H Russell Bernard2 Author Orcid Image ;   Roger B Fillingim3 Author Orcid Image
Article Authors Cited by (33) Tweetations (65) Metrics

Journals

  1. Ryu Y. Text-Mining of Online Discourse to Characterize the Nature of Pain in Low Back Pain. Journal of The Korean Society of Physical Medicine 2019;14(3):55 View
  2. Jayaraman P, Forkan A, Morshed A, Haghighi P, Kang Y. Healthcare 4.0: A review of frontiers in digital health. WIREs Data Mining and Knowledge Discovery 2020;10(2) View
  3. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  4. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  5. Tougas M, Chambers C, Corkum P, Robillard J, Gruzd A, Howard V, Kampen A, Boerner K, Hundert A. Social Media Content About Children’s Pain and Sleep: Content and Network Analysis. JMIR Pediatrics and Parenting 2018;1(2):e11193 View
  6. Delir Haghighi P, Kang Y, Buchbinder R, Burstein F, Whittle S. Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter. JMIR Public Health and Surveillance 2017;3(1):e4 View
  7. Sewalk K, Tuli G, Hswen Y, Brownstein J, Hawkins J. Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study. Journal of Medical Internet Research 2018;20(10):e10043 View
  8. Lee H, McAuley J, Hübscher M, Allen H, Kamper S, Moseley G. Tweeting back: predicting new cases of back pain with mass social media data. Journal of the American Medical Informatics Association 2016;23(3):644 View
  9. Manganello J, Falisi A, Roberts K, Smith K, McKenzie L. Pediatric injury information seeking for mothers with young children: The role of health literacy and ehealth literacy. Journal of Communication in Healthcare 2016;9(3):223 View
  10. Gohil S, Vuik S, Darzi A. Sentiment Analysis of Health Care Tweets: Review of the Methods Used. JMIR Public Health and Surveillance 2018;4(2):e43 View
  11. Doan S, Ritchart A, Perry N, Chaparro J, Conway M. How Do You #relax When You’re #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health and Surveillance 2017;3(2):e35 View
  12. Hajiabadi M, Zare H, Bobarshad H. IEDC: An integrated approach for overlapping and non-overlapping community detection. Knowledge-Based Systems 2017;123:188 View
  13. Baumgartner P, Peiper N. Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users. Substance Abuse: Research and Treatment 2017;11 View
  14. Mullins C, ffrench-O'Carroll R, Lane J, O'Connor T. Sharing the pain: an observational analysis of Twitter and pain in Ireland. Regional Anesthesia & Pain Medicine 2020;45(8):597 View
  15. 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
  16. Peiper N, Baumgartner P, Chew R, Hsieh Y, Bieler G, Bobashev G, Siege C, Zarkin G. Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California. Journal of Medical Internet Research 2017;19(7):e236 View
  17. Elphinston R, Scotti Requena S, Angus D, de Andrade D, Freeman C, Day M. The Promotion of Policy Changes Restricting Access to Codeine Medicines on Twitter: What do National Pain Organizations Say?. The Journal of Pain 2020;21(7-8):881 View
  18. Metwally O, Blumberg S, Ladabaum U, Sinha S. Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study. Journal of Medical Internet Research 2017;19(6):e200 View
  19. Dreisbach C, Koleck T, Bourne P, Bakken S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. International Journal of Medical Informatics 2019;125:37 View
  20. Zhang P, Bhaskarabhatla S. How advocacy affects Twitter migraine conversations: A pilot cross-sectional survey of Northeast American “migraine” landscape on Twitter from May to June 2020. Cephalalgia Reports 2020;3 View
  21. Pavan Kumar C, Dhinesh Babu L. Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks. Journal of Intelligent & Fuzzy Systems 2021;40(6):11749 View
  22. Johannes José Fijen L, Joaquín López González J, Treur J. An adaptive temporal-causal network model to analyse extinction of communication over time. Cognitive Systems Research 2021;68:73 View
  23. Oyebode O, Lomotey R, Orji R. “I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis. IEEE Access 2021;9:61074 View
  24. Alattar F, Shaalan K. Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media. IEEE Access 2021;9:61756 View
  25. Almorox E, Stokes J, Morciano M. Has COVID-19 changed carer's views of health and care integration in care homes? A sentiment difference-in-difference analysis of on-line service reviews. Health Policy 2022;126(11):1117 View
  26. He L, Yin T, Zheng K. They May Not Work! An evaluation of eleven sentiment analysis tools on seven social media datasets. Journal of Biomedical Informatics 2022;132:104142 View
  27. Berger S, Baria A. Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches. Frontiers in Pain Research 2022;3 View
  28. 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
  29. Vinod P, Sheeja S. Sentiment prediction model in social media data using beluga dodger optimization-based ensemble classifier. Social Network Analysis and Mining 2023;13(1) View
  30. Morgner C, Dening K, Dening T, Gibson B. An alternative theoretical approach to develop a new conception about pain in people with dementia. Medical Humanities 2024;50(3):545 View