Electronic cigarettes (or e-cigarettes) are currently a popular emerging tobacco product. Because e-cigarettes do
not generate toxic tobacco combustion products produced when smoking regular cigarettes, they are perceived
and sometimes promoted as a less harmful alternative to smoking and also as a means to quit smoking. Although
they may be less harmful, the efﬁcacy of using them for smoking cessation has not been demonstrated conclu-
sively with studies indicating evidence both favoring and opposing such an application for them. Furthermore,
owing to their recent introduction, there are also safety concerns given reported adverse events. The US Federal
Drug Administration (FDA) has introduced regulations that went into effect on 8/8/2016 requiring FDA review for
e-cigarette products, banning sales to minors and free samples, and requiring warning labels on certain prod-
ucts. In this context, surveillance of evolving themes and factors contributing to message popularity for e-cigarette
chatter on social media platforms is an important activity. Twitter has become the favorite network for teenagers
and young adults owing to the short message size and associated ease of use on smart phones. For an emerg-
ing product like e-cigarettes, the asymmetric follower-friend connections and hashtag functionality in Twitter offer
a convenient way to propagate information and facilitate discussion. Among online forums, Reddit allows for
longer messages from users inviting speciﬁc feedback from other users. Within Reddit, the e-cigarette subreddit
facilitates focused discussions on e-cigarette use and products. In this project, we propose to computationally
analyze the contents and user proﬁles available in the dataset of all e-cigarette tweets generated during 7/2016–
6/2017 and all e-cigarette subreddit posts/comments generated since 9/2016. We will continue such analyses
with data collected through free but rate limited API throughout the duration of the project. Our ﬁrst aim is to sur-
face speciﬁc themes of interest directly from e-cigarette messages using phrase based online and binned topic
models. We expect these themes to complement familiar broad themes that researchers currently consider when
analyzing online messages. Next, we will identify factors (involving message content and proﬁle characteristics)
that contribute to different notions of popularity (#retweets, #replies, #up-votes) of e-cigarette tweets/messages.
We expect these results will help health agencies, the FDA, and researchers gain insights into observed viral
nature of certain messages and designing effective strategies to maximize diffusion of their messages. Finally,
we will conduct these analyses along the dimensions of gender, race, and age to grasp variations in themes and
popularity factors speciﬁc to different vulnerable demographic segments.
If you are accessing this page during weekend or evening hours, the database may currently be offline for maintenance and should operational within a few hours. Otherwise, we have been notified of this error and will be addressing it immediately.
Please contact us
if this error persists.
We apologize for the inconvenience.
- The DCCPS Team.