Literature DB >> 28782062

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

Sifei Han1, Ramakanth Kavuluru2,1.   

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.

Entities:  

Year:  2016        PMID: 28782062      PMCID: PMC5540097          DOI: 10.1007/978-3-319-47874-6_22

Source DB:  PubMed          Journal:  Soc Inform (2016)


  18 in total

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7.  Tobacco Use Among Middle and High School Students--United States, 2011-2015.

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8.  Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study.

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9.  A cross-sectional examination of marketing of electronic cigarettes on Twitter.

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10.  Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter.

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3.  Tweeting about public health policy: Social media response to the UK Government's announcement of a Parliamentary vote on draft standardised packaging regulations.

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6.  Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification.

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7.  Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study.

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8.  Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

Authors:  Shyam Visweswaran; Jason B Colditz; Patrick O'Halloran; Na-Rae Han; Sanya B Taneja; Joel Welling; Kar-Hai Chu; Jaime E Sidani; Brian A Primack
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  8 in total

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