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Grant Details

Grant Number: 7R01CA225773-03 Interpret this number
Primary Investigator: Primack, Brian
Organization: University Of Arkansas At Fayetteville
Project Title: Leveraging Twitter to Monitor Nicotine and Tobacco Cancer Communication
Fiscal Year: 2020


Patterns in Twitter data have revolutionized understanding of public health events such as influenza outbreaks. While researchers have begun to examine messaging related to substance use on Twitter, this project will strengthen the use of Twitter as an infoveillance tool to more rigorously examine nicotine, tobacco, and cancer- related communication. Twitter is particularly suited to this work because its users are commonly adolescents, young adults, and racial and ethnic minorities, all of whom are at increased risk for nicotine and tobacco product (NTP) use and related health consequences. Additionally, due to the openness of the platform, searches are replicable and transparent, enabling large-scale systematic research. Therefore, our multidisciplinary team of experts in diverse relevant fields—including public health, behavioral science, computational linguistics, computer science, biomedical informatics, and information privacy and security—will build upon our previous research to develop and validate structured algorithms providing automated surveillance of Twitter’s multifaceted and continuously evolving information related to NTPs. First, we will qualitatively assess a stratified random sample of relevant NTP-related tweets for specific coded variables, such as the message’s primary sentiment and other key information of potential value (e.g., whether a message involves buying/selling, policy/law, and cancer-related communication). Tweets will be obtained directly from Twitter using software we developed that leverages a comprehensive list of Twitter-optimized search strings related to NTPs. Second, we will statistically determine what message characteristics (e.g., the presence of certain words, punctuation, and/or structures) are most strongly associated with each of the coded variables for each search string. Using this information, we will create specialized Machine Learning (ML) algorithms based on state-of-the-art methods from Natural Language Processing (NLP) to automatically assess and categorize future Twitter data. Third, we will use this information to provide automatic assessment of current and future streaming data. Time series analyses using seasonal Auto-Regressive Integrated Moving Averages (ARIMA) will determine if there are significant changes over time in volume of messaging related to each specific coded variables of interest. Trends will be examined at the daily, weekly, and monthly level, because each of these levels is potentially valuable for intervention. To maximize the translational value of this project, we will partner with public health department stakeholders who are experts in streamlining dissemination of actionable trends data. In summary, this project will substantially advance our understanding of representations of NTPs on social media—as well as our ability to conduct automated surveillance and analysis of this content. This project will result in important and concrete deliverables, including open-source algorithms for future researchers and processes to quickly disseminate actionable data for tailoring community- level interventions.


Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study.
Authors: Baker W. , Colditz J.B. , Dobbs P.D. , Mai H. , Visweswaran S. , Zhan J. , Primack B.A. .
Source: JMIR medical informatics, 2022-07-21; 10(7), p. e33678.
EPub date: 2022-07-21.
PMID: 35862172
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Analyzing Twitter Chatter About Tobacco Use Within Intoxication-related Contexts of Alcohol Use: "Can Someone Tell Me Why Nicotine is So Fire When You're Drunk?".
Authors: Russell A.M. , Colditz J.B. , Barry A.E. , Davis R.E. , Shields S. , Ortega J.M. , Primack B. .
Source: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2022-07-13; 24(8), p. 1193-1200.
PMID: 34562100
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Discussions and Misinformation About Electronic Nicotine Delivery Systems and COVID-19: Qualitative Analysis of Twitter Content.
Authors: Sidani J.E. , Hoffman B. , Colditz J.B. , Wolynn R. , Hsiao L. , Chu K.H. , Rose J.J. , Shensa A. , Davis E. , Primack B. .
Source: JMIR formative research, 2022-04-13; 6(4), p. e26335.
EPub date: 2022-04-13.
PMID: 35311684
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Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets.
Authors: Chu K.H. , Hershey T.B. , Hoffman B.L. , Wolynn R. , Colditz J.B. , Sidani J.E. , Primack B.A. .
Source: Journal of medical Internet research, 2022-03-25; 24(3), p. e27894.
EPub date: 2022-03-25.
PMID: 35333188
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Policy and Behavior: Comparisons between Twitter Discussions about the US Tobacco 21 Law and Other Age-Related Behaviors.
Authors: Dobbs P.D. , Colditz J.B. , Shields S. , Meadows A. , Primack B.A. .
Source: International journal of environmental research and public health, 2022-02-24; 19(5), .
EPub date: 2022-02-24.
PMID: 35270306
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Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions.
Authors: Dobbs P.D. , Schisler E. , Colditz J.B. , Primack B.A. .
Source: Tobacco control, 2022-02-16; , .
EPub date: 2022-02-16.
PMID: 35173067
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E-Cigarette-Related Nicotine Misinformation on Social Media.
Authors: Sidani J.E. , Hoffman B.L. , Colditz J.B. , Melcher E. , Taneja S.B. , Shensa A. , Primack B. , Davis E. , Chu K.H. .
Source: Substance use & misuse, 2022; 57(4), p. 588-594.
EPub date: 2022-01-22.
PMID: 35068338
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Collaborative Public Health Strategies to Combat e-Cigarette Regulation Loopholes.
Authors: Chu K.H. , Hershey T.B. , Sidani J.E. .
Source: JAMA pediatrics, 2021-11-01; 175(11), p. 1102-1104.
PMID: 34398212
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Re-evaluating standards of human subjects protection for sensitive health data in social media networks.
Authors: Chu K.H. , Colditz J. , Sidani J. , Zimmer M. , Primack B. .
Source: Social networks, 2021 Oct; 67, p. 41-46.
EPub date: 2019-11-20.
PMID: 34539049
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#Alcohol: Portrayals of Alcohol in Top Videos on TikTok.
Authors: Russell A.M. , Davis R.E. , Ortega J.M. , Colditz J.B. , Primack B. , Barry A.E. .
Source: Journal of studies on alcohol and drugs, 2021 Sep; 82(5), p. 615-622.
PMID: 34546908
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#DoctorsSpeakUp: Lessons learned from a pro-vaccine Twitter event.
Authors: Hoffman B.L. , Colditz J.B. , Shensa A. , Wolynn R. , Taneja S.B. , Felter E.M. , Wolynn T. , Sidani J.E. .
Source: Vaccine, 2021-05-06; 39(19), p. 2684-2691.
EPub date: 2021-04-13.
PMID: 33863574
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Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.
Authors: Visweswaran S. , Colditz J.B. , O'Halloran P. , Han N.R. , Taneja S.B. , Welling J. , Chu K.H. , Sidani J.E. , Primack B.A. .
Source: Journal of medical Internet research, 2020-08-12; 22(8), p. e17478.
EPub date: 2020-08-12.
PMID: 32784184
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Integrating Social Dynamics Into Modeling Cigarette and E-Cigarette Use.
Authors: Chu K.H. , Shensa A. , Colditz J.B. , Sidani J.E. , Hoffman B.L. , Sinclair D. , Krauland M.G. , Primack B.A. .
Source: Health education & behavior : the official publication of the Society for Public Health Education, 2020 Apr; 47(2), p. 191-201.
EPub date: 2020-02-24.
PMID: 32090652
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JUUL on Twitter: Analyzing Tweets About Use of a New Nicotine Delivery System.
Authors: Sidani J.E. , Colditz J.B. , Barrett E.L. , Chu K.H. , James A.E. , Primack B.A. .
Source: The Journal of school health, 2020 Feb; 90(2), p. 135-142.
EPub date: 2019-12-11.
PMID: 31828791
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I wake up and hit the JUUL: Analyzing Twitter for JUUL nicotine effects and dependence.
Authors: Sidani J.E. , Colditz J.B. , Barrett E.L. , Shensa A. , Chu K.H. , James A.E. , Primack B.A. .
Source: Drug and alcohol dependence, 2019-11-01; 204, p. 107500.
EPub date: 2019-08-30.
PMID: 31499242
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Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking.
Authors: Chu K.H. , Colditz J. , Malik M. , Yates T. , Primack B. .
Source: Journal of medical Internet research, 2019-07-08; 21(7), p. e12443.
EPub date: 2019-07-08.
PMID: 31287063
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JUUL: Spreading Online and Offline.
Authors: Chu K.H. , Colditz J.B. , Primack B.A. , Shensa A. , Allem J.P. , Miller E. , Unger J.B. , Cruz T.B. .
Source: The Journal of adolescent health : official publication of the Society for Adolescent Medicine, 2018 Nov; 63(5), p. 582-586.
PMID: 30348280
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Toward Real-Time Infoveillance of Twitter Health Messages.
Authors: Colditz J.B. , Chu K.H. , Emery S.L. , Larkin C.R. , James A.E. , Welling J. , Primack B.A. .
Source: American journal of public health, 2018 Aug; 108(8), p. 1009-1014.
EPub date: 2018-06-21.
PMID: 29927648
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