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Grant Details

Grant Number: 5R01CA192240-03 Interpret this number
Primary Investigator: Kim, Annice
Organization: Research Triangle Institute
Project Title: Using Social Media Data for E-Cigarette Surveillance and Policy Research
Fiscal Year: 2016


Abstract

DESCRIPTION (provided by applicant): Social media data give public health researchers unprecedented opportunity to understand emerging use patterns, consequences, and contextual factors related to alcohol, tobacco, and other drugs (ATOD). Electronic cigarettes (e-cigarettes) are rapidly proliferating in an unregulated environment and increasingly used by adults and youth despite limited evidence of their safety. The proposed study examines the nature of e-cigarette information shared on Twitter and links individual-level Twitter data to survey data to examine the extent to which individuals are exposed to and share this information and whether social media posts reflect actual perceptions and behaviors. Results will inform our understanding of e-cigarettes and the utility of social media data for surveillance and regulation of ATOD. Aims. Aim 1: Using text mining and sentiment analysis, characterize information about e-cigarettes shared on Twitter to understand (a) consumer perceptions and use; (b) advertisers' marketing strategies, including spamming behavior; and (c) changing federal, state, or local laws. Aim 2: Link individual-level Twitter data to self-reported survey daa to (a) describe the amount and type of e-cigarette information that current smokers and e-cigarette users are potentially exposed to and share, (b) assess whether information exposure is associated with recall and information sharing, and (c) validate whether information shared on Twitter reflects actual perceptions and use. Aim 1: Tweets about e-cigarettes will be identified using an iteratively updated search syntax to capture the changing terminology of e-cigarettes. Text mining and sentiment analysis will be conducted using supervised and unsupervised algorithms. Computational methods that detect abnormal patterns in tweeting behavior, networks, and post content will be employed to identify spamming or fake consumers. The algorithms developed in Year 1 will be refined quarterly in Years 2 and 3 to characterize emerging topics. Aim 2: Using a rotating panel design, a national convenience sample of 1,800 adult current smokers and 1,800 dual e-cigarette and cigarette users will be recruited to complete six baseline and follow-up surveys, with two being rapid response surveys focused on policy or regulatory action. We will analyze public Twitter posts of respondents and their Twitter networks and the extent and type of e-cigarette messages they were potentially exposed to and shared. The survey will measure participants' recall of e-cigarette messages from their Twitter network, e-cigarette-related perceptions and use, and cessation behaviors. We will link Twitter data to survey data to validate social media posts and assess whether information exposure is associated with recall and information sharing and how these patterns and relationships vary by respondent characteristics (e.g., e-cigarette use, quit intentions, social media use).



Publications

An Experimental Study of Nicotine Warning Statements in E-cigarette Tweets.
Authors: Guillory J. , Kim A.E. , Fiacco L. , Cress M. , Pepper J. , Nonnemaker J. .
Source: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2020-04-21; 22(5), p. 814-821.
PMID: 30820571
Related Citations

Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure.
Authors: Murphy J. , Hsieh Y.P. , Wenger M. , Kim A.E. , Chew R. .
Source: Information, communication and society, 2019; 22(5), p. 622-636.
EPub date: 2019-01-16.
PMID: 32982569
Related Citations

Classification of Twitter Users Who Tweet About E-Cigarettes.
Authors: Kim A. , Miano T. , Chew R. , Eggers M. , Nonnemaker J. .
Source: JMIR public health and surveillance, 2017-09-26; 3(3), p. e63.
EPub date: 2017-09-26.
PMID: 28951381
Related Citations

Predicting age groups of Twitter users based on language and metadata features.
Authors: Morgan-Lopez A.A. , Kim A.E. , Chew R.F. , Ruddle P. .
Source: PloS one, 2017; 12(8), p. e0183537.
EPub date: 2017-08-29.
PMID: 28850620
Related Citations

Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers.
Authors: Guillory J. , Kim A. , Murphy J. , Bradfield B. , Nonnemaker J. , Hsieh Y. .
Source: Journal of medical Internet research, 2016-11-15; 18(11), p. e288.
EPub date: 2016-11-15.
PMID: 27847353
Related Citations

Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets.
Authors: Shu L. , Liu B. , Xu H. , Kim A. .
Source: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 2016 Nov; 2016, p. 225-235.
PMID: 29756130
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