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探索推特上电子烟产品营销预测因素:基于时间序列的信息流行病学方法

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

作者信息

Ezike Nnamdi C, Ames Boykin Allison, Dobbs Page D, Mai Huy, Primack Brian A

机构信息

College of Education and Health Professions University of Arkansas Fayetteville, AR United States.

College of Engineering University of Arkansas Fayetteville, AR United States.

出版信息

JMIR Infodemiology. 2022 Jul 22;2(2):e37412. doi: 10.2196/37412. eCollection 2022 Jul-Dec.

Abstract

BACKGROUND

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve 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.

摘要

背景

电子尼古丁传送系统(即电子烟)会增加新接触烟草者,尤其是青少年和年轻成年人出现不良健康后果的风险。这一弱势群体也面临电子烟品牌营销和在社交媒体上打广告的风险。了解电子烟制造商如何进行社交媒体广告和营销的预测因素,有助于采取公共卫生方法来应对电子烟使用问题。

目的

本研究使用时间序列建模技术记录预测电子烟商业推文每日频率变化的因素。

方法

我们分析了2017年1月1日至2020年12月31日期间收集的关于电子烟商业推文每日频率的数据。我们将数据拟合到自回归积分移动平均(ARIMA)模型和未观测成分模型(UCM)。四项指标评估模型预测准确性。UCM中的预测因素包括与美国食品药品监督管理局(FDA)相关事件的日子、具有重大意义的非FDA相关事件(如学术或新闻公告)、工作日与周末,以及JUUL保持活跃推特账户(即从其公司推特账户积极发推)的时期与JUUL停止发推的时期。

结果

当将这两种统计模型拟合到数据时,结果表明UCM是最适合我们数据的建模技术。UCM中包含的所有四个预测因素都是电子烟商业推文每日频率的显著预测因素。平均而言,与没有FDA相关事件的日子相比,在有FDA相关事件的日子里,推特上电子烟的品牌广告和营销量高出150多条。同样,与没有此类事件的日子相比,在有重要非FDA事件的日子里,平均记录的关于电子烟的商业推文超过40条。我们还发现,工作日关于电子烟的商业推文比周末更多,并且当JUUL保持活跃推特账户时商业推文更多。

结论

电子烟公司在推特上推广其产品。在FDA发布重要公告的日子里,商业推文发布的可能性显著更高,这可能会改变FDA所分享信息的叙述方式。美国仍需要对电子烟产品的数字营销进行监管。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb5/9987194/1a316057be9c/infodemiology_v2i2e37412_fig1.jpg

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