P
Page D. Dobbs Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Federal Grant PI
Researcher
faculty
14 h-index
93 pubs
625 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Page D. Dobbs' research focuses on public health issues related to tobacco and nicotine products, particularly among young adults. Dobbs has investigated the effectiveness of policies such as Tobacco 21 and examined user motivations for engaging with products like JUULs, as evidenced by publications in these areas. Current federally funded projects include an NIH award of $162,051 to study loopholes, enforcement challenges, and tobacco industry interference with tobacco control policies, with Dobbs serving as PI. Additionally, a $1,000,000 NSF grant, for which Dobbs is Co-PI, aims to develop a large-scale multi-modality learning system to identify tobacco addiction and predictive analytics via social media platforms.
Dobbs' work also extends to utilizing advanced computational methods for analyzing social media data. Publications such as "Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study" and "Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition" demonstrate an interest in applying machine learning and deep learning techniques to understand health-related online discourse and behaviors. Collaborations include extensive work with researchers at the University of Arkansas at Fayetteville, including Pha Nguyen, Eric D. Schisler, Han‐Seok Seo, and Alexander Nelson, with whom Dobbs has co-authored multiple publications.
With a career total of 93 publications and 602 citations, Dobbs has an h-index of 13. The research group led by Dobbs is actively engaged in these areas, with recent activity indicated by publications as recent as 2024 and a potential 2026 publication.
Metrics
- h-index: 14
- Publications: 93
- Citations: 625
Selected Publications
-
Cooling the Conversation: Discourse About Menthol and Flavored Tobacco Restrictions on TikTok (2026)
-
The experiences of young adults attempting to quit e-cigarettes: A mixed-methods analysis (2025)
-
Online Interest in Elf Bar in the United States: Google Health Trends Analysis (2024)
-
Do-It-Yourself Flavored Capsule Cigarettes: Exploiting Potential Regulatory Loopholes? (2024)
-
Tobacco control policies discussed on social media: a scoping review (2024)
-
Patient–provider communication about cigarette and e-cigarette use during pregnancy: Adaptation and validation of frequency and quality of communication measures among a sample of pregnant patients (2024)
-
React: recognize every action everywhere all at once (2024)
-
‘Cashing in’ nicotine pouches for prizes (2024)
-
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos (2024)
-
#Discreetshipping: Selling E-cigarettes on TikTok (2024)
-
Cigarette and E-Cigarette Harm Perceptions During Pregnancy (2024)
-
REACT: Recognize Every Action Everywhere All At Once (2024)
-
Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis (Preprint) (2023)
-
Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis (2023)
-
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition (2023)
Federal Grants 2 $1,162,051 total
NIH/NIH Office of the Director
Contact PI
Sep 2022 - Aug 2027
Loopholes, Enforcement Challenges, and Tobacco Industry Interference with Tobacco Control Policies
Collaboration Network
Top Collaborators
Khoa Luu
University of Arkansas at Fayetteville
10 shared publications
In database
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- React: recognize every action everywhere all at once
- REACT: Recognize Every Action Everywhere All At Once
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
Showing 5 of 10 shared publications
Brian A. Primack
Oregon State University (US)
9 shared publications
- Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study
- Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions
- Policy and Behavior: Comparisons between Twitter Discussions about the US Tobacco 21 Law and Other Age-Related Behaviors
- Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis
- Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series
Showing 5 of 9 shared publications
Pha Nguyen
University of Arkansas at Fayetteville
9 shared publications
In database
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- React: recognize every action everywhere all at once
- REACT: Recognize Every Action Everywhere All At Once
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
Showing 5 of 9 shared publications
Jason B. Colditz
University of Pittsburgh (US)
6 shared publications
- Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study
- Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions
- Policy and Behavior: Comparisons between Twitter Discussions about the US Tobacco 21 Law and Other Age-Related Behaviors
- Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis
- Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study (Preprint)
Showing 5 of 6 shared publications
Eric D. Schisler
Salem State University
6 shared publications
In database
- #Discreetshipping: Selling E-cigarettes on TikTok
- Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions
- A reasoned action approach to pregnant smokers’ intention to switch to e-cigarettes: Does educational attainment influence theory application?
- Cigarette and E-Cigarette Harm Perceptions During Pregnancy
- Patient–provider communication about cigarette and e-cigarette use during pregnancy: Adaptation and validation of frequency and quality of communication measures among a sample of pregnant patients
Showing 5 of 6 shared publications
Sarah B. Maness
5 shared publications
- Human papillomavirus (HPV) knowledge, beliefs, and vaccine uptake among United States and international college students
- Pregnancy-Specific Stress and Racial Discrimination Among U.S. Women
- Interventions Using mHealth Strategies to Improve Screening Rates of Cervical Cancer: A Scoping Review
- Expansion of Programs and Schools Offering Dual Degrees in Master of Public Health and Master of Divinity: A Call to Action
- HPV Knowledge, HPV Vaccine Knowledge, and HPV Beliefs Survey
Marshall K. Cheney
University of Oklahoma (US)
5 shared publications
- Tobacco 21 Policies in the U.S.: The Importance of Local Control With Federal Policy
- College Students’ Reasons for Using JUULs
- Harm perceptions, JUUL dependence, and other tobacco product use among young adults who use JUUL
- Intention to Quit Using JUUL Scale
- College Students’ Use of Social Media and E-Cigarettes: How Correctly Identifying Platform Type Influences Findings
Nnamdi Ezike
University of Arkansas at Fayetteville
5 shared publications
In database
- Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis
- Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series
- Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series (Preprint)
- Predicting Sentiment of Tweets towards Electronic Cigarettes Using the Unobserved Component Model (Preprint)
- Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis (Preprint)
Allison J. Ames
University of Arkansas at Fayetteville (US)
5 shared publications
- Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis
- Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series
- Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series (Preprint)
- Predicting Sentiment of Tweets towards Electronic Cigarettes Using the Unobserved Component Model (Preprint)
- Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis (Preprint)
Naga VS Raviteja Chappa
University of Arkansas at Fayetteville (US)
5 shared publications
- Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- REACT: Recognize Every Action Everywhere All At Once
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
- REACT: Recognize Every Action Everywhere All At Once
Alexander Nelson
University of Arkansas at Fayetteville
5 shared publications
In database
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
Han‐Seok Seo
University of Arkansas at Fayetteville
5 shared publications
In database
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
Naga Venkata Sai Raviteja Chappa
University of Arkansas at Fayetteville
5 shared publications
In database
- SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
- React: recognize every action everywhere all at once
- HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos
- SoGAR: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition
- Advanced Deep Learning Techniques for Tobacco Usage Assessment in TikTok Videos
Huy Mai
University of Arkansas at Fayetteville
4 shared publications
In database
- Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study
- Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series
- Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study (Preprint)
- Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series (Preprint)
Lisa Henriksen
Stanford University (US)
4 shared publications
- ‘Cashing in’ nicotine pouches for prizes
- Tobacco control policies discussed on social media: a scoping review
- Preemption in State Tobacco Minimum Legal Sales Age Laws in the US, 2022: A Policy Analysis of State Statutes and Case Laws
- Do-It-Yourself Flavored Capsule Cigarettes: Exploiting Potential Regulatory Loopholes?
Similar Researchers
Based on overlapping research topics
Tricia Starks
University of Arkansas at Fayetteville
Smoking Behavior and Cessation
Health Policy Implementation Science
Public Health Research
Page D. Dobbs
University of Arkansas for Medical Sciences
Smoking Behavior and Cessation
Health Policy Implementation Science
Media Studies and Communication
Sandilyn Bullock
University of Arkansas for Medical Sciences
Smoking Behavior and Cessation
Media Studies and Communication
Public Health Research
Shelby Shields
University of Arkansas at Fayetteville
Smoking Behavior and Cessation
Media Studies and Communication
Substance Abuse Treatment and Outcomes
Erik Buchholz
University of Arkansas at Little Rock
Smoking Behavior and Cessation
Media Studies and Communication
Substance Abuse Treatment and Outcomes
Connor McCurtain
University of Arkansas at Fayetteville
Advanced Neural Network Applications
Media Studies and Communication
Substance Abuse Treatment and Outcomes