Sex Differences in Hookah-Related Images Posted on Tumblr: A Content Analysis

Primack, Brian A.; Carroll, Mary V.; Shensa, Ariel; Davis, Wesley D.; Levine, Michele D.

doi:10.1080/10810730.2015.1095814

Cited by 27 publications

(36 citation statements)

References 53 publications

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“…This is reflected in the tobacco products used by “social smoker” young adults and the social nature of tobacco use in this group. Findings from our study highlight that past 30-day hookah use was nearly twice as high in the “social smoker” class as in the “smoker” class in line with studies noting the social nature of hookah use in young adults (Castaneda, Barnett, Soule, & Young, 2016; Sharma, Beck, & Clark, 2013; Sharma, Clark, & Sharp, 2014) and the depiction of social aspects of hookah use on social media (Carroll, Shensa, & Primack, 2013; Primack, Carroll, Shensa, Davis, & Levine, 2016). Of note, recent qualitative research supports the role of social modeling in hookah initiation among young adults (Castaneda et al, 2016).…”

Section: Discussionsupporting

confidence: 88%

Identifying “social smoking” U.S. young adults using an empirically-driven approach

Villanti

Johnson

Rath

et al. 2017

Addictive Behaviors

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The phenomenon of “social smoking” emerged in the past decade as an important area of research, largely due to its high prevalence in young adults. The purpose of this study was to identify classes of young adult ever smokers based on measures of social and contextual influences on tobacco use. Latent class models were developed using social smoking measures, and not the frequency or quantity of tobacco use. Data come from a national sample of young adult ever smokers aged 18–24 (Truth Initiative Young Adult Cohort Study, N = 1,564). The optimal models identified three latent classes: Class 1 – Nonsmokers (52%); Class 2 – Social smokers (18%); and Class 3 – Smokers (30%). Nearly 60% of the “social smoker” class self-identified as a social smoker, 30% as an ex-smoker/tried smoking, and 12% as a non-smoker. The “social smoker” class was most likely to report using tobacco mainly or only with others. Past 30-day cigarette use was highest in the “smoker” class. Hookah use was highest in the “social smoker” class. Other tobacco and e-cigarette use was similar in the “social smoker” and “smoker” classes. Past 30-day tobacco and e-cigarette use was present for all products in the “non-smoker” class. Young adult social smokers emerge empirically as a sizable, distinct class from other smokers, even without accounting for tobacco use frequency or intensity. The prevalence of hookah use in “social smokers” indicates a group for which the social aspect of tobacco use could drive experimentation and progression to regular use.

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Section: Discussionsupporting

confidence: 88%

Identifying “social smoking” U.S. young adults using an empirically-driven approach

Villanti

Johnson

Rath

et al. 2017

Addictive Behaviors

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“…Journal of Healthcare Communications ISSN 2472-1654 was similar to findings on other social media platforms: Pinterest [25], Twitter [26,27], and Tumblr [28]. In this way, the portrayal of WPS on Instagram resembles the portrayal of smoking in the movies before the general surgeon launched a nationwide antismoking campaign [29].…”

Section: Archivos De Medicina Issn 1698-9465supporting

confidence: 66%

#Hookah, #Shisha, #Narghile: the Portrayal of #Waterpipe Smoking on Instagram

Haddad¹

2017

J Healthc Commun

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Background: In the last 25 years, rates of waterpipe tobacco smoking have sharply increased in the United States and other Western countries. At the same time, social media platforms such as Instagram have become a popular way to portray content related to waterpipe smoking. Thus, public health researchers have become interested in the potential relationship between the portrayal of waterpipe smoking on social media and the attitudes of users toward the activity.

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“…The root terms could have appeared in the post or in an accompanying hashtag, for example, hookah or #hookah. While the word waterpipe is commonly used in academic papers and presentations to refer to hookah, it is uncommon for individuals to use this term on social media and it was thus not included in this study [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Identifying Sentiment of Hookah-Related Posts on Twitter

Allem¹,

Ramanujam²,

Lerman³

et al. 2017

JMIR Public Health Surveill

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BackgroundThe increasing popularity of hookah (or waterpipe) use in the United States and elsewhere has consequences for public health because it has similar health risks to that of combustible cigarettes. While hookah use rapidly increases in popularity, social media data (Twitter, Instagram) can be used to capture and describe the social and environmental contexts in which individuals use, perceive, discuss, and are marketed this tobacco product. These data may allow people to organically report on their sentiment toward tobacco products like hookah unprimed by a researcher, without instrument bias, and at low costs.ObjectiveThis study describes the sentiment of hookah-related posts on Twitter and describes the importance of debiasing Twitter data when attempting to understand attitudes.MethodsHookah-related posts on Twitter (N=986,320) were collected from March 24, 2015, to December 2, 2016. Machine learning models were used to describe sentiment on 20 different emotions and to debias the data so that Twitter posts reflected sentiment of legitimate human users and not of social bots or marketing-oriented accounts that would possibly provide overly positive or overly negative sentiment of hookah.ResultsFrom the analytical sample, 352,116 tweets (59.50%) were classified as positive while 177,537 (30.00%) were classified as negative, and 62,139 (10.50%) neutral. Among all positive tweets, 218,312 (62.00%) were classified as highly positive emotions (eg, active, alert, excited, elated, happy, and pleasant), while 133,804 (38.00%) positive tweets were classified as passive positive emotions (eg, contented, serene, calm, relaxed, and subdued). Among all negative tweets, 95,870 (54.00%) were classified as subdued negative emotions (eg, sad, unhappy, depressed, and bored) while the remaining 81,667 (46.00%) negative tweets were classified as highly negative emotions (eg, tense, nervous, stressed, upset, and unpleasant). Sentiment changed drastically when comparing a corpus of tweets with social bots to one without. For example, the probability of any one tweet reflecting joy was 61.30% from the debiased (or bot free) corpus of tweets. In contrast, the probability of any one tweet reflecting joy was 16.40% from the biased corpus.ConclusionsSocial media data provide researchers the ability to understand public sentiment and attitudes by listening to what people are saying in their own words. Tobacco control programmers in charge of risk communication may consider targeting individuals posting positive messages about hookah on Twitter or designing messages that amplify the negative sentiments. Posts on Twitter communicating positive sentiment toward hookah could add to the normalization of hookah use and is an area of future research. Findings from this study demonstrated the importance of debiasing data when attempting to understand attitudes from Twitter data.

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Sex Differences in Hookah-Related Images Posted on Tumblr: A Content Analysis

Cited by 27 publications

References 53 publications

Identifying “social smoking” U.S. young adults using an empirically-driven approach

Identifying “social smoking” U.S. young adults using an empirically-driven approach

#Hookah, #Shisha, #Narghile: the Portrayal of #Waterpipe Smoking on Instagram

Identifying Sentiment of Hookah-Related Posts on Twitter

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