Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter.

Sifei Han, Ramakanth Kavuluru
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Abstract

Electronic cigarettes (e-cigs) have been gaining popularity and have emerged as a controversial tobacco product since their introduction in 2007 in the U.S. The smoke-free aspect of e-cigs renders them less harmful than conventional cigarettes and is one of the main reasons for their use by people who plan to quit smoking. The US food and drug administration (FDA) has introduced new regulations early May 2016 that went into effect on August 8, 2016. Given this important context, in this paper, we report results of a project to identify current themes in e-cig tweets in terms of semantic interpretations of topics generated with topic modeling. Given marketing/advertising tweets constitute almost half of all e-cig tweets, we first build a classifier that identifies marketing and non-marketing tweets based on a hand-built dataset of 1000 tweets. After applying the classifier to a dataset of over a million tweets (collected during 4/2015 - 6/2016), we conduct a preliminary content analysis and run topic models on the two sets of tweets separately after identifying the appropriate numbers of topics using topic coherence. We interpret the results of the topic modeling process by relating topics generated to specific e-cig themes. We also report on themes identified from e-cig tweets generated at particular places (such as schools and churches) for geo-tagged tweets found in our dataset using the GeoNames API. To our knowledge, this is the first effort that employs topic modeling to identify e-cig themes in general and in the context of geo-tagged tweets tied to specific places of interest.

Abstract Image

Twitter 上电子烟营销和非营销主题的探索性分析。
电子香烟(e-cigs)自 2007 年在美国推出以来,受到越来越多人的欢迎,并成为一种备受争议的烟草产品。电子香烟的无烟特性使其危害低于传统香烟,这也是计划戒烟的人使用电子香烟的主要原因之一。美国食品和药物管理局(FDA)于2016年5月初出台了新法规,并于2016年8月8日正式生效。鉴于这一重要背景,我们在本文中报告了一个项目的成果,该项目旨在通过对主题建模生成的主题进行语义解释来识别电子烟推文中的当前主题。鉴于营销/广告推文几乎占了所有电子烟推文的一半,我们首先根据手工建立的 1000 条推文数据集建立了一个分类器,用于识别营销和非营销推文。在将分类器应用于超过 100 万条推文(收集于 2015 年 4 月至 2016 年 6 月)的数据集后,我们进行了初步的内容分析,并在使用主题一致性识别出适当数量的主题后,分别对两组推文运行主题模型。我们将生成的话题与特定的电子烟主题联系起来,从而解释话题建模过程的结果。我们还报告了从特定地点(如学校和教堂)产生的电子烟推文中识别出的主题,这些推文是使用 GeoNames API 在数据集中找到的带有地理标记的推文。据我们所知,这是首次采用主题建模来识别一般电子烟主题以及与特定兴趣场所相关的地理标记推文。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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