探索Twitter上预测电子烟产品营销的因素:使用时间序列的信息流行病学方法。

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2022-07-01 DOI:10.2196/37412
Nnamdi C Ezike, Allison Ames Boykin, Page D Dobbs, Huy Mai, Brian A Primack
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引用次数: 0

摘要

背景:电子尼古丁输送系统(称为电子烟或电子烟)增加了naïve烟草使用者,特别是青年和青壮年不良健康结果的风险。这些弱势群体也面临着社交媒体上暴露的品牌营销和电子烟广告的风险。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,有助于解决电子烟使用问题的公共卫生方法。目的:本研究使用时间序列建模技术记录了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日收集的关于电子烟的商业推文的每日频率数据。我们将数据拟合到自回归综合移动平均(ARIMA)模型和未观测分量模型(UCM)中。四项措施评估模型预测的准确性。UCM中的预测因子包括与美国食品和药物管理局(FDA)相关事件的天数,与非FDA相关的重要事件,如学术或新闻公告,工作日与周末,以及JUUL保持活跃Twitter帐户(即从其公司Twitter帐户积极发推文)与JUUL停止发推文的时间。结果:两种统计模型对数据进行拟合,结果表明UCM是我们数据的最佳建模技术。UCM中包含的所有4个预测因子都是关于电子烟的商业推文每日频率的重要预测因子。平均而言,在有FDA相关活动的日子里,推特上的电子烟品牌广告和营销广告比没有FDA相关活动的日子多150多个。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,平均记录了40多条关于电子烟的商业推文。我们还发现,在工作日,有关电子烟的商业推文比周末更多,而当JUUL保持活跃的推特账户时,商业推文也更多。结论:电子烟公司在Twitter上推广他们的产品。商业推特更有可能在FDA发布重要公告的日子发布,这可能会改变FDA分享信息的叙述。在美国,仍然需要对电子烟产品的数字营销进行监管。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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.

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.

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.

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