S
Shu-Hong Zhu
Researcher at University of California, San Diego
Publications - 123
Citations - 7138
Shu-Hong Zhu is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Smoking cessation & Quitline. The author has an hindex of 36, co-authored 116 publications receiving 6391 citations. Previous affiliations of Shu-Hong Zhu include Colorado Department of Public Health and Environment & Alliant International University.
Papers
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Journal ArticleDOI
Exploring Consumer Attitudes towards Hookah, Cigarettes, and Cigars Using Twitter
Journal ArticleDOI
Evaluation of the Tobacco-Use Prevention Education (TUPE) program in California.
Sara B. McMenamin,Sharon E. Cummins,Yue-Lin Zhuang,Anthony Gamst,Carlos G. Ruiz,Antonio Mayoral,Shu-Hong Zhu +6 more
TL;DR: TUPE funding was associated with an increase in schools’ tobacco-specific prevention activities and these enhanced activities were associated with lower tobacco use among students, and education and prevention efforts regarding emerging tobacco products need to be strengthened across all schools.
Journal ArticleDOI
Combining Text Mining and Data Visualization Techniques to Understand Consumer Experiences of Electronic Cigarettes and Hookah in Online Forums
TL;DR: This work applies text mining and novel visualization techniques to textual data derived from online health discussion forums in order to better understand consumers experiences and perceptions of electronic cigarettes and hookah.
Journal ArticleDOI
Targeting Nonsmokers to Help Smokers Quit: Features of a Large-scale Intervention.
TL;DR: An innovative approach that targeted nonsmokers through a media-style campaign with repeated reminders about smoking cessation and showed it to be effective in helping smokers quit in a large randomized trial.
Journal ArticleDOI
Content Analysis of Tobacco-related Twitter Posts
TL;DR: Twitter surveillance further reveals opportunities for education: positive sentiment toward the term “hookah” but negative sentiment toward “tobacco” suggests a disconnect in users’ perceptions of hookah’s health effects, and machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, allowing for automated tobacco surveillance applications.