Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series

被引:2
|
作者
Ezike, Nnamdi C. [1 ]
Boykin, Allison Ames [1 ]
Dobbs, Page [1 ]
Mai, Huy [2 ]
Primack, Brian A. [3 ]
机构
[1] Univ Arkansas, Coll Educ & Hlth Profess, 751 W Maple St, Fayetteville, AR 72701 USA
[2] Univ Arkansas, Coll Engn, Fayetteville, AR USA
[3] Oregon State Univ, Coll Publ Hlth & Human Sci, Corvallis, OR USA
来源
JMIR INFODEMIOLOGY | 2022年 / 2卷 / 02期
关键词
tobacco; electronic cigarettes; social media; marketing; time series; youth; young adults; infodemiology; infoveillance; digital marketing; advertising; Twitter; promote; e-cigarette; EXPOSURE; TOBACCO; MEDIA;
D O I
10.2196/37412
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naive tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use.Objective: This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods: We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting.Results: When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account.Conclusions: e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.
引用
收藏
页数:14
相关论文
共 48 条
  • [1] Using marketing frameworks to predict the effects of e-cigarette commercials on youth
    Pike, James Russell
    Miller, Stephen
    Cappelli, Christopher
    Tan, Nasya
    Xie, Bin
    Stacy, Alan W. W.
    YOUNG CONSUMERS, 2023, 24 (02): : 149 - 164
  • [2] Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter
    Han, Sifei
    Kavuluru, Ramakanth
    SOCIAL INFORMATICS, PT II, 2016, 10047 : 307 - 322
  • [3] Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study
    Kim, Annice E.
    Hopper, Timothy
    Simpson, Sean
    Nonnemaker, James
    Lieberman, Alicea J.
    Hansen, Heather
    Guillory, Jamie
    Porter, Lauren
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2015, 17 (11)
  • [4] Exploring the e-cigarette e-commerce marketplace: Identifying Internet e-cigarette marketing characteristics and regulatory gaps
    Mackey, Tim K.
    Miner, Angela
    Cuomo, Raphael E.
    DRUG AND ALCOHOL DEPENDENCE, 2015, 156 : 97 - 103
  • [5] The Need for a Policy That Bans the Use of Cartoons in Marketing E-cigarette Products
    Seitz, Christopher M.
    Orsini, Muhsin Michael
    Jung, Grace
    Butler, Kate
    NICOTINE & TOBACCO RESEARCH, 2020, 22 (10) : 1932 - 1933
  • [6] What factors predict the passage of state-level e-cigarette regulations?
    Maclean, Johanna Catherine
    Oney, Melissa
    Marti, Joachim
    Sindelar, Jody
    HEALTH ECONOMICS, 2018, 27 (05) : 897 - 907
  • [7] Quantifying Cigarette and e-Cigarette Marketing Exposure Among Chinese Adolescents Using Ecological Momentary Assessment
    Czaplicki, Lauren
    Barker, Hannah E.
    Thrul, Johannes
    Cui, Yuxian
    Yang, Tingzhong
    Cohen, Joanna E.
    NICOTINE & TOBACCO RESEARCH, 2024, 26 (11) : 1480 - 1488
  • [8] A machine learning approach to predict e-cigarette use and dependence among Ontario youth
    Shi, Jiamin
    Fu, Rui
    Hamilton, Hayley
    Chaiton, Michael
    HEALTH PROMOTION AND CHRONIC DISEASE PREVENTION IN CANADA-RESEARCH POLICY AND PRACTICE, 2022, 42 (01): : 21 - 28
  • [9] Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision
    Vassey, Julia
    Kennedy, Chris J.
    Herbert Chang, Ho-Chun
    Smith, Ashley S.
    Unger, Jennifer B.
    NICOTINE & TOBACCO RESEARCH, 2023, : 552 - 560
  • [10] Identifying E-cigarette Content on TikTok: Using a BERTopic Modeling Approach
    Lee, Juhan
    Ouellette, Rachel R.
    Murthy, Dhiraj
    Pretzer, Ben
    Anand, Tanvi
    Kong, Grace
    NICOTINE & TOBACCO RESEARCH, 2024, 27 (01) : 91 - 96