An Analysis of Twitter Data on E-cigarette Sentiments and Promotion

Godea, Andreea; Caragea, Cornelia; Bulgarov, Florin Adrian; Ramisetty-Mikler, Suhasini

doi:10.1007/978-3-319-19551-3_27

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…As such, building and using a classifier that separates marketing tweets is an important pre-processing step in several efforts. We are aware of at least four such efforts [10,14,18,20] on building automatic classifiers for e-cig marketing tweets for various end-goals. Other researchers who studied e-cig tweets focused on sentiment analysis [14,30] and diffusion of messages from e-cig brands on Twitter [8].…”

Section: Introductionmentioning

confidence: 99%

“…We are aware of at least four such efforts [10,14,18,20] on building automatic classifiers for e-cig marketing tweets for various end-goals. Other researchers who studied e-cig tweets focused on sentiment analysis [14,30] and diffusion of messages from e-cig brands on Twitter [8]. In our current effort

We manually estimated the proportion of marketing and non-marketing tweets to be 48.6 % (45.5–51.7 %) : 51.4 % (48.3–54.5 %) from a sample of 1000 randomly selected tweets selected from over one million e-cig tweets collected through Twitter streaming API between 4/2015 and 6/2016 (Sect.

…”

Section: Introductionmentioning

confidence: 99%

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

Han¹,

Kavuluru²

2016

Lecture Notes in Computer Science

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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.

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Section: Introductionmentioning

confidence: 99%

…”

Section: Introductionmentioning

confidence: 99%

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

Han¹,

Kavuluru²

2016

Lecture Notes in Computer Science

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“…The ratio of direct tweets (18%) shows also a significant expected reaction from consumers, by replying or following advice, warnings, requests or invitations. Similar to us, Godea et al [9] identify tweets' purposes in healthcare (advertising, informational, positive or negative opinions). However, we focus on intentions as established by pragmatics, ensuring thus a domain-independent, general approach.…”

Section: Resultsmentioning

confidence: 82%

Analyzing Perceived Intentions of Public Health-Related Communication on Twitter

Epure

Deneckère

Salinesi

2017

Artificial Intelligence in Medicine

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The increasing population with chronic diseases and highly engaged in online communication has triggered an urge in healthcare to understand this phenomenon. We propose an automatic approach to analyze the perceived intentions behind public tweets. Our long-term goal is to create high-level, behavioral models of the health information consumers and disseminators, relevant to studies in narrative medicine and health information dissemination. The contributions of this paper are: 1) a validated intention taxonomy, derived from pragmatics and empirically adjusted to Twitter public communication; 2) a tagged health-related corpus of 1100 tweets; 3) an effective approach to automatically discover intentions from text, using supervised machine learning with discourse features only, independent of domain vocabulary. Reasoning on the results, we claim the transferability of our solution to other healthcare corpora, enabling thus more extensive studies in the concerned domains.

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“…Additionally, the limitations of the machine learning classifier have been explicitly mentioned by the authors as their future work; however, it does not talk about any concrete intervention. [17] proposes a supervised approach to identifying sentiments and information dissemination concerning e-cigarettes from Twitter data. Again, it is limited to analysis and doesn't provide solution for scalable public health interventions.…”

Section: A Machine Learning In Behavior Classificationmentioning

confidence: 99%

Social Bots for Online Public Health Interventions

Deb

Majmundar

Seo

et al. 2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related diseases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either protobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting protobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign.

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An Analysis of Twitter Data on E-cigarette Sentiments and Promotion

Cited by 11 publications

References 11 publications

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

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

Analyzing Perceived Intentions of Public Health-Related Communication on Twitter

Social Bots for Online Public Health Interventions

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