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

Letters (85) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570) Smoking Cessation (518) Validity, Reliability, and Bias (89)

Published on in Vol 18, No 8 (2016): August

The Importance of Debiasing Social Media Data to Better Understand E-Cigarette-Related Attitudes and Behaviors

The Importance of Debiasing Social Media Data to Better Understand E-Cigarette-Related Attitudes and Behaviors

The Importance of Debiasing Social Media Data to Better Understand E-Cigarette-Related Attitudes and Behaviors

Authors of this article:

Jon-Patrick Allem1 Author Orcid Image ;   Emilio Ferrara2 Author Orcid Image
Article Authors Cited by (44) Tweetations (19) Metrics

Journals

  1. Reuter K, Angyan P, Le N, MacLennan A, Cole S, Bluthenthal R, Lane C, El-Khoueiry A, Buchanan T. Monitoring Twitter Conversations for Targeted Recruitment in Cancer Trials in Los Angeles County: Protocol for a Mixed-Methods Pilot Study. JMIR Research Protocols 2018;7(9):e177 View
  2. Allem J, Escobedo P, Dharmapuri L. Cannabis Surveillance With Twitter Data: Emerging Topics and Social Bots. American Journal of Public Health 2020;110(3):357 View
  3. Stens O, Weisman M, Simard J, Reuter K. Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis. JMIR Research Protocols 2020;9(8):e15623 View
  4. Allem J, Majmundar A, Dharmapuri L, Cruz T, Unger J. E-liquid-related posts to Twitter in 2018: Thematic analysis. Addictive Behaviors Reports 2019;10:100196 View
  5. Pozzana I, Ferrara E. Measuring Bot and Human Behavioral Dynamics. Frontiers in Physics 2020;8 View
  6. Allem J, Dharmapuri L, Leventhal A, Unger J, Boley Cruz T. Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis. Journal of Medical Internet Research 2018;20(11):e11669 View
  7. Allem J, Dharmapuri L, Unger J, Cruz T. Characterizing JUUL-related posts on Twitter. Drug and Alcohol Dependence 2018;190:1 View
  8. Kim Y, Huang J, Emery S. The Research Topic Defines “Noise” in Social Media Data – a Response from the Authors. Journal of Medical Internet Research 2017;19(6):e165 View
  9. Ahmed W, Vidal-Alaball J, Downing J, López Seguí F. COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data. Journal of Medical Internet Research 2020;22(5):e19458 View
  10. Hatchard J, Quariguasi Frota Neto J, Vasilakis C, Evans-Reeves K, Fu K. Tweeting about public health policy: Social media response to the UK Government’s announcement of a Parliamentary vote on draft standardised packaging regulations. PLOS ONE 2019;14(2):e0211758 View
  11. Allem J, Ferrara E, Uppu S, Cruz T, Unger J. E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends. JMIR Public Health and Surveillance 2017;3(4):e98 View
  12. Yang K, Varol O, Davis C, Ferrara E, Flammini A, Menczer F. Arming the public with artificial intelligence to counter social bots. Human Behavior and Emerging Technologies 2019;1(1):48 View
  13. Bunyan A, Venuturupalli S, Reuter K. Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study. JMIR Research Protocols 2021;10(5):e15716 View
  14. Unger J, Rogers C, Barrington-Trimis J, Majmundar A, Sussman S, Allem J, Soto D, Cruz T. “I’m using cigarettes to quit JUUL”: An analysis of Twitter posts about JUUL cessation. Addictive Behaviors Reports 2020;12:100286 View
  15. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  16. Hammond A, Paul M, Hobelmann J, Koratana A, Dredze M, Chisolm M. Perceived Attitudes About Substance Use in Anonymous Social Media Posts Near College Campuses: Observational Study. JMIR Mental Health 2018;5(3):e52 View
  17. 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
  18. 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
  19. 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
  20. Kudugunta S, Ferrara E. Deep neural networks for bot detection. Information Sciences 2018;467:312 View
  21. Budhwani H, Sun R. Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data. Journal of Medical Internet Research 2020;22(5):e19301 View
  22. Aramburu M, Berlanga R, Lanza I. Social Media Multidimensional Analysis for Intelligent Health Surveillance. International Journal of Environmental Research and Public Health 2020;17(7):2289 View
  23. 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
  24. Allem J, Uppu S, Boley Cruz T, Unger J. Characterizing Swisher Little Cigar–Related Posts on Twitter in 2018: Text Analysis. Journal of Medical Internet Research 2019;21(7):e14398 View
  25. Allem J, Ramanujam J, Lerman K, Chu K, Boley Cruz T, Unger J. Identifying Sentiment of Hookah-Related Posts on Twitter. JMIR Public Health and Surveillance 2017;3(4):e74 View
  26. Majmundar A, Allem J, Cruz T, Unger J. Where Do People Vape? Insights from Twitter Data. International Journal of Environmental Research and Public Health 2019;16(17):3056 View
  27. McCausland K, Maycock B, Leaver T, Wolf K, Freeman B, Thomson K, Jancey J. E-Cigarette Promotion on Twitter in Australia: Content Analysis of Tweets. JMIR Public Health and Surveillance 2020;6(4):e15577 View
  28. 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
  29. He L, Yin T, Hu Z, Chen Y, Hanauer D, Zheng K. Developing a standardized protocol for computational sentiment analysis research using health-related social media data. Journal of the American Medical Informatics Association 2021;28(6):1125 View
  30. Reuter K, Deodhar A, Makri S, Zimmer M, Berenbaum F, Nikiphorou E. The impact of the COVID-19 pandemic on people with rheumatic and musculoskeletal diseases: insights from patient-generated data on social media. Rheumatology 2021;60(SI):SI77 View
  31. Reuter K, Lee D. Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis. JMIR Research Protocols 2021;10(2):e13731 View
  32. Allem J, Dormanesh A, Majmundar A, Unger J, Kirkpatrick M, Choube A, Aithal A, Ferrara E, Boley Cruz T. Topics of Nicotine-Related Discussions on Twitter: Infoveillance Study. Journal of Medical Internet Research 2021;23(6):e25579 View
  33. Majmundar A, Allem J, Cruz T, Unger J, Pentz M. Twitter Surveillance at the Intersection of the Triangulum. Nicotine & Tobacco Research 2022;24(1):118 View
  34. Cai M, Luo H, Meng X, Cui Y, Wang W. Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 2023;60(2):103197 View
  35. Allem J, Majmundar A, Dormanesh A, Donaldson S. Identifying Health-Related Discussions of Cannabis Use on Twitter by Using a Medical Dictionary: Content Analysis of Tweets. JMIR Formative Research 2022;6(2):e35027 View
  36. Khaund T, Kirdemir B, Agarwal N, Liu H, Morstatter F. Social Bots and Their Coordination During Online Campaigns: A Survey. IEEE Transactions on Computational Social Systems 2022;9(2):530 View
  37. Donaldson S, Dormanesh A, Majmundar A, Pérez C, Lopez H, Saghian M, Beard T, Unger J, Allem J. Examining the Peer-Reviewed Literature on Tobacco-Related Social Media Data: Scoping Review. Nicotine and Tobacco Research 2024;26(4):413 View
  38. Wenjia Y, Yitian W, Xinyu L, Jinyue G, Yiyuan N. Comparing the video information about the HPV vaccine: a content analysis between Bilibili and YouTube. Health & New Media Research 2020;4(2):169 View

Books/Policy Documents

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