Published on in Vol 17, No 8 (2015): August

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Journals

  1. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  2. Li Q, Wang C, Liu R, Wang L, Zeng D, Leischow S. Understanding Users’ Vaping Experiences from Social Media: Initial Study Using Sentiment Opinion Summarization Techniques. Journal of Medical Internet Research 2018;20(8):e252 View
  3. Visweswaran S, Colditz J, O’Halloran P, Han N, Taneja S, Welling J, Chu K, Sidani J, Primack B. Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study. Journal of Medical Internet Research 2020;22(8):e17478 View
  4. Mackey T, Kalyanam J, Katsuki T, Lanckriet G. Twitter-Based Detection of Illegal Online Sale of Prescription Opioid. American Journal of Public Health 2017;107(12):1910 View
  5. Waring M, Baker K, Peluso A, May C, Pagoto S. Content analysis of Twitter chatter about indoor tanning. Translational Behavioral Medicine 2019;9(1):41 View
  6. Martinez L, Hughes S, Walsh-Buhi E, Tsou M. “Okay, We Get It. You Vape”: An Analysis of Geocoded Content, Context, and Sentiment regarding E-Cigarettes on Twitter. Journal of Health Communication 2018;23(6):550 View
  7. Walsh E, Busby Grant J. Detecting Temporal Cognition in Text: Comparison of Judgements by Self, Expert and Machine. Frontiers in Psychology 2018;9 View
  8. Hornik R. Measuring Campaign Message Exposure and Public Communication Environment Exposure: Some Implications of the Distinction in the Context of Social Media. Communication Methods and Measures 2016;10(2-3):167 View
  9. Gibson L, Siegel L, Kranzler E, Volinsky A, O’Donnell M, Williams S, Yang Q, Kim Y, Binns S, Tran H, Maidel Epstein V, Leffel T, Jeong M, Liu J, Lee S, Emery S, Hornik R. Combining Crowd-Sourcing and Automated Content Methods to Improve Estimates of Overall Media Coverage: Theme Mentions in E-cigarette and Other Tobacco Coverage. Journal of Health Communication 2019;24(12):889 View
  10. Sanders C, Nahar P, Small N, Hodgson D, Ong B, Dehghan A, Sharp C, Dixon W, Lewis S, Kontopantelis E, Daker-White G, Bower P, Davies L, Kayesh H, Spencer R, McAvoy A, Boaden R, Lovell K, Ainsworth J, Nowakowska M, Shepherd A, Cahoon P, Hopkins R, Allen D, Lewis A, Nenadic G. Digital methods to enhance the usefulness of patient experience data in services for long-term conditions: the DEPEND mixed-methods study. Health Services and Delivery Research 2020;8(28):1 View
  11. Park E, Chang H, Nam H. Use of Machine Leaning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. Journal of Medical Internet Research 2017;19(4):e120 View
  12. Palomino M, Taylor T, Göker A, Isaacs J, Warber S. The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”. International Journal of Environmental Research and Public Health 2016;13(1):142 View
  13. Chu K, Unger J, Allem J, Pattarroyo M, Soto D, Cruz T, Yang H, Jiang L, Yang C, Bauch C. Diffusion of Messages from an Electronic Cigarette Brand to Potential Users through Twitter. PLOS ONE 2015;10(12):e0145387 View
  14. Kim K, Gibson L, Williams S, Kim Y, Binns S, Emery S, Hornik R. Valence of Media Coverage About Electronic Cigarettes and Other Tobacco Products From 2014 to 2017: Evidence From Automated Content Analysis. Nicotine & Tobacco Research 2020;22(10):1891 View
  15. Daniulaityte R, Chen L, Lamy F, Carlson R, Thirunarayan K, Sheth A. “When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets. JMIR Public Health and Surveillance 2016;2(2):e162 View
  16. Chu K, Allem J, Unger J, Cruz T, Akbarpour M, Kirkpatrick M. Strategies to find audience segments on Twitter for e-cigarette education campaigns. Addictive Behaviors 2019;91:222 View
  17. Chu K, Colditz J, Malik M, Yates T, Primack B. Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking. Journal of Medical Internet Research 2019;21(7):e12443 View
  18. 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
  19. Leung R. Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172 View
  20. Lazard A, Saffer A, Wilcox G, Chung A, Mackert M, Bernhardt J. E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter. JMIR Public Health and Surveillance 2016;2(2):e171 View
  21. Lienemann B, Unger J, Cruz T, Chu K. Methods for Coding Tobacco-Related Twitter Data: A Systematic Review. Journal of Medical Internet Research 2017;19(3):e91 View
  22. Lazard A, Wilcox G, Tuttle H, Glowacki E, Pikowski J. Public reactions to e-cigarette regulations on Twitter: a text mining analysis. Tobacco Control 2017;26(e2):e112 View
  23. Zhan Y, Liu R, Li Q, Leischow S, Zeng D. Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms. Journal of Medical Internet Research 2017;19(1):e24 View
  24. Beaunoyer E, Arsenault M, Lomanowska A, Guitton M. Understanding online health information: Evaluation, tools, and strategies. Patient Education and Counseling 2017;100(2):183 View
  25. Kim Y, Kim J. Using photos for public health communication: A computational analysis of the Centers for Disease Control and Prevention Instagram photos and public responses. Health Informatics Journal 2020;26(3):2159 View
  26. Lee S, Liu J, Gibson L, Hornik R. Rating the Valence of Media Content about Electronic Cigarettes Using Crowdsourcing: Testing Rater Instructions and Estimating the Optimal Number of Raters. Health Communication 2021;36(4):497 View
  27. 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
  28. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  29. 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
  30. Wang D, Lyu J, Zhao X. Public Opinion About E-Cigarettes on Chinese Social Media: A Combined Study of Text Mining Analysis and Correspondence Analysis. Journal of Medical Internet Research 2020;22(10):e19804 View
  31. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660 View
  32. Helmstetter S, Paulheim H. Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision. Future Internet 2021;13(5):114 View
  33. Tsai M, Wang Y. Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19. International Journal of Environmental Research and Public Health 2021;18(12):6272 View
  34. Jongenelis M, Jongenelis G, Alexander E, Kennington K, Phillips F, Pettigrew S, Smith J. A content analysis of the tweets of e‐cigarette proponents in Australia. Health Promotion Journal of Australia 2022;33(2):445 View
  35. Xu Q, Yang J, Haupt M, Cai M, Nali M, Mackey T. Digital Surveillance to Identify California Alternative and Emerging Tobacco Industry Policy Influence and Mobilization on Facebook. International Journal of Environmental Research and Public Health 2021;18(21):11150 View
  36. Rahim A, Ibrahim M, Chua S, Musa K. Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier. Healthcare 2021;9(12):1679 View
  37. A. Rahim A, Ibrahim M, Musa K, Chua S, Yaacob N. Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews. International Journal of Environmental Research and Public Health 2021;18(18):9912 View
  38. Hassan L, Elkaref M, de Mel G, Bogdanovica I, Nenadic G. Text mining tweets on e-cigarette risks and benefits using machine learning following a vaping related lung injury outbreak in the USA. Healthcare Analytics 2022;2:100066 View
  39. Rahim A, Ibrahim M, Musa K, Chua S, Yaacob N. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare 2021;9(10):1369 View
  40. Baker W, Colditz J, Dobbs P, Mai H, Visweswaran S, Zhan J, Primack B. Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study. JMIR Medical Informatics 2022;10(7):e33678 View
  41. Fu R, Kundu A, Mitsakakis N, Elton-Marshall T, Wang W, Hill S, Bondy S, Hamilton H, Selby P, Schwartz R, Chaiton M. Machine learning applications in tobacco research: a scoping review. Tobacco Control 2023;32(1):99 View
  42. Haupt M, Xu Q, Yang J, Cai M, Mackey T. Characterizing Vaping Industry Political Influence and Mobilization on Facebook: Social Network Analysis. Journal of Medical Internet Research 2021;23(10):e28069 View
  43. Elkaim L, Levett J, Niazi F, Alvi M, Shlobin N, Linzey J, Robertson F, Bokhari R, Alotaibi N, Lasry O. Cervical Myelopathy and Social Media: Mixed Methods Analysis. Journal of Medical Internet Research 2023;25:e42097 View
  44. Kim K. Scanned information exposure and support for tobacco regulations among US youth and young adult tobacco product users and non-users. Health Education Research 2023;38(5):426 View
  45. Lossio-Ventura J, Weger R, Lee A, Guinee E, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Mental Health 2024;11:e50150 View

Books/Policy Documents

  1. Zhang H, Wheldon C, Tao C, Dunn A, Guo Y, Huo J, Bian J. Social Web and Health Research. View
  2. Aboelmaged M, Thomas S, Elsheikh S. Handbook of Research on Healthcare Administration and Management. View
  3. Martinez L, Tsou M, Spitzberg B. Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics. View