Mining social media data for opinion polarities about electronic cigarettes

Dai, Hongying; Hao, Jingbin

doi:10.1136/tobaccocontrol-2015-052818

Cited by 40 publications

(28 citation statements)

References 24 publications

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“…Approximately 1% of all users in the analytical sample provided data that allowed us to describe the geographic areas in which e-cigarette–related discussions took place in the United States. While this is a small percentage, it is compatible with earlier work [ 25 , 40 ] and represents 36,549 users in the United States. Additionally, we did not have the necessary demographic information (eg, age) of Twitter users to consider population density and age distributions of geographic areas.…”

Section: Discussionsupporting

confidence: 88%

“…Twitter has been used in tobacco control research with studies showing how tobacco education campaigns can be informed by monitoring tweets [ 11 , 12 ] and which e-cigarette–related messages are likely to spread on Twitter [ 13 ], among other studies [ 14 - 21 ]. Ayers and colleagues [ 22 ] recently analyzed a sample of e-cigarette–related tweets and reported that social image was the most identified reason for e-cigarette use in 2015.…”

Section: Introductionmentioning

confidence: 99%

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E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends

Allem¹,

Ferrara²,

Uppu³

et al. 2017

JMIR Public Health Surveill

139

105

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BackgroundAs e-cigarette use rapidly increases in popularity, data from online social systems (Twitter, Instagram, Google Web Search) can be used to capture and describe the social and environmental context in which individuals use, perceive, and are marketed this tobacco product. Social media data may serve as a massive focus group where people organically discuss e-cigarettes unprimed by a researcher, without instrument bias, captured in near real time and at low costs.ObjectiveThis study documents e-cigarette–related discussions on Twitter, describing themes of conversations and locations where Twitter users often discuss e-cigarettes, to identify priority areas for e-cigarette education campaigns. Additionally, this study demonstrates the importance of distinguishing between social bots and human users when attempting to understand public health–related behaviors and attitudes.MethodsE-cigarette–related posts on Twitter (N=6,185,153) were collected from December 24, 2016, to April 21, 2017. Techniques drawn from network science were used to determine discussions of e-cigarettes by describing which hashtags co-occur (concept clusters) in a Twitter network. Posts and metadata were used to describe where geographically e-cigarette–related discussions in the United States occurred. Machine learning models were used to distinguish between Twitter posts reflecting attitudes and behaviors of genuine human users from those of social bots. Odds ratios were computed from 2x2 contingency tables to detect if hashtags varied by source (social bot vs human user) using the Fisher exact test to determine statistical significance.ResultsClusters found in the corpus of hashtags from human users included behaviors (eg, #vaping), vaping identity (eg, #vapelife), and vaping community (eg, #vapenation). Additional clusters included products (eg, #eliquids), dual tobacco use (eg, #hookah), and polysubstance use (eg, #marijuana). Clusters found in the corpus of hashtags from social bots included health (eg, #health), smoking cessation (eg, #quitsmoking), and new products (eg, #ismog). Social bots were significantly more likely to post hashtags that referenced smoking cessation and new products compared to human users. The volume of tweets was highest in the Mid-Atlantic (eg, Pennsylvania, New Jersey, Maryland, and New York), followed by the West Coast and Southwest (eg, California, Arizona and Nevada).ConclusionsSocial media data may be used to complement and extend the surveillance of health behaviors including tobacco product use. Public health researchers could harness these data and methods to identify new products or devices. Furthermore, findings from this study demonstrate the importance of distinguishing between Twitter posts from social bots and humans when attempting to understand attitudes and behaviors. Social bots may be used to perpetuate the idea that e-cigarettes are helpful in cessation and to promote new products as they enter the marketplace.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends

Allem¹,

Ferrara²,

Uppu³

et al. 2017

JMIR Public Health Surveill

139

105

View full text Add to dashboard Cite

show abstract

“…732,746 ENDS are promoted heavily online 451,695 through ENDS company–sponsored advertisements 697 and users’ social media profiles, 424 with occurrences on YouTube 690,697,706,707,710,722,731,739 and Twitter. 691,713,715,725–727,733,743 Youth and young adult exposure to TV advertisements for ENDS has increased since 2011. 689,735 Two RCTs 712,720 and four cross-sectional studies 403,629,723,741 have shown that tobacco marketing exposure may promote ENDS uptake.…”

Section: Evidence Synthesismentioning

confidence: 99%

Overview of Electronic Nicotine Delivery Systems: A Systematic Review

Glasser

Collins

Pearson

et al. 2017

American Journal of Preventive Medicine

418

332

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Context Rapid developments in e-cigarettes, or electronic nicotine delivery systems (ENDS), and the evolution of the overall tobacco product marketplace warrant frequent evaluation of the published literature. The purpose of this article is to report updated findings from a comprehensive review of the published scientific literature on ENDS. Evidence acquisition The authors conducted a systematic review of published empirical research literature on ENDS through May 31, 2016, using a detailed search strategy in the PubMed electronic database, expert review, and additional targeted searches. Included studies presented empirical findings and were coded to at least one of nine topics: (1) Product Features; (2) Health Effects; (3) Consumer Perceptions; (4) Patterns of Use; (5) Potential to Induce Dependence; (6) Smoking Cessation; (7) Marketing and Communication; (8) Sales; and (9) Policies; reviews and commentaries were excluded. Data from included studies were extracted by multiple coders (October 2015 to August 2016) into a standardized form and synthesized qualitatively by topic. Evidence synthesis There were 686 articles included in this systematic review. The majority of studies assessed patterns of ENDS use and consumer perceptions of ENDS, followed by studies examining health effects of vaping and product features. Conclusions Studies indicate that ENDS are increasing in use, particularly among current smokers, pose substantially less harm to smokers than cigarettes, are being used to reduce/quit smoking, and are widely available. More longitudinal studies and controlled trials are needed to evaluate the impact of ENDS on population-level tobacco use and determine the health effects of longer-term vaping.

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“…Different from the posts from other mainstream social media like Twitter and Reddit, JuiceDB posts are more focused on the e-liquid features [7]. The rich information in the review data and advanced sentiment analytics have made mining and summarizing e-cigarette information from the unstructured texts possible [8]. …”

Section: Introductionmentioning

confidence: 99%

Mining online e-liquid reviews for opinion polarities about e-liquid features

Chen

Zeng

2017

BMC Public Health

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BackgroundIn recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users.MethodsWe collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users’ preference on this feature.ResultsThe popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit.ConclusionsWe identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users.

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Mining social media data for opinion polarities about electronic cigarettes

Cited by 40 publications

References 24 publications

E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends

E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends

Overview of Electronic Nicotine Delivery Systems: A Systematic Review

Mining online e-liquid reviews for opinion polarities about e-liquid features

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