Dataset Information

User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study.

ABSTRACT: BACKGROUND:The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. OBJECTIVE:This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. METHODS:We applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette-related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette-related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. RESULTS:More than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. CONCLUSIONS:The most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.

SUBMITTER: Lu X

PROVIDER: S-EPMC7380993 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study.

Lu Xinyi X Chen Long L Yuan Jianbo J Luo Joyce J Luo Jiebo J Xie Zidian Z Li Dongmei D

Journal of medical Internet research 20200624 6

<h4>Background</h4>The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media.<h4>Objective</h4>This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user ...[more]

PMID: 32579123

Similar Datasets

Project description:BACKGROUND:Electronic cigarette (e-cigarette) is an emerging product with a rapid-growth market in recent years. Social media has become an important platform for information seeking and sharing. We aim to mine hidden topics from e-cigarette datasets collected from different social media platforms. OBJECTIVE:This paper aims to gain a systematic understanding of the characteristics of various types of social media, which will provide deep insights into how consumers and policy makers effectively use social media to track e-cigarette-related content and adjust their decisions and policies. METHODS:We collected data from Reddit (27,638 e-cigarette flavor-related posts from January 1, 2011, to June 30, 2015), JuiceDB (14,433 e-juice reviews from June 26, 2013 to November 12, 2015), and Twitter (13,356 "e-cig ban"-related tweets from January, 1, 2010 to June 30, 2015). Latent Dirichlet Allocation, a generative model for topic modeling, was used to analyze the topics from these data. RESULTS:We found four types of topics across the platforms: (1) promotions, (2) flavor discussions, (3) experience sharing, and (4) regulation debates. Promotions included sales from vendors to users, as well as trades among users. A total of 10.72% (2,962/27,638) of the posts from Reddit were related to trading. Promotion links were found between social media platforms. Most of the links (87.30%) in JuiceDB were related to Reddit posts. JuiceDB and Reddit identified consistent flavor categories. E-cigarette vaping methods and features such as steeping, throat hit, and vapor production were broadly discussed both on Reddit and on JuiceDB. Reddit provided space for policy discussions and majority of the posts (60.7%) holding a negative attitude toward regulations, whereas Twitter was used to launch campaigns using certain hashtags. Our findings are based on data across different platforms. The topic distribution between Reddit and JuiceDB was significantly different (P<.001), which indicated that the user discussions focused on different perspectives across the platforms. CONCLUSIONS:This study examined Reddit, JuiceDB, and Twitter as social media data sources for e-cigarette research. These mined findings could be further used by other researchers and policy makers. By utilizing the automatic topic-modeling method, the proposed unified feedback model could be a useful tool for policy makers to comprehensively consider how to collect valuable feedback from social media.

Project description:BackgroundFlavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online.ObjectiveThis study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021.MethodsWe collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette-related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets.ResultsThrough our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period.ConclusionsOur proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors.

Project description:BACKGROUND:In recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users' perspective. OBJECTIVE:This study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. METHODS:A dataset consisting of 2.8 million e-cigarette-related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. RESULTS:Temporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. CONCLUSIONS:This study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors.

Project description:BackgroundElectronic cigarettes (e-cigarettes) have been widely promoted on the internet, and subsequently, social media has been used as an important informative platform by e-cigarette users. Beliefs and knowledge expressed on social media platforms have largely influenced e-cigarette uptake, the decision to switch from conventional smoking to e-cigarette smoking, and positive and negative connotations associated with e-cigarettes. Despite this, there is a gap in our knowledge of people's perceptions and sentiments on e-cigarettes as depicted on social media platforms.ObjectiveThis study aimed to (1) provide an overview of studies examining the perceptions and sentiments associated with e-cigarettes on social media platforms and online discussion forums, (2) explore people's perceptions of e-cigarette therein, and (3) examine the methodological limitations and gaps of the included studies.MethodsSearches in major electronic databases, including PubMed, Cumulative Index of Nursing and Allied Health Literature, EMBASE, Web of Science, and Communication and Mass Media Complete, were conducted using the following search terms: "electronic cigarette," "electronic vaporizer," "electronic nicotine," and "electronic nicotine delivery systems" combined with "internet," "social media," and "internet use." The studies were selected if they examined participants' perceptions and sentiments of e-cigarettes on online forums or social media platforms during the 2007-2017 period.ResultsA total of 21 articles were included. A total of 20 different social media platforms and online discussion forums were identified. A real-time snapshot and characteristics of sentiments, personal experience, and perceptions toward e-cigarettes on social media platforms and online forums were identified. Common topics regarding e-cigarettes included positive and negative health effects, testimony by current users, potential risks, benefits, regulations associated with e-cigarettes, and attitude toward them as smoking cessation aids.ConclusionsAlthough perceptions among social media users were mixed, there were more positive sentiments expressed than negative ones. This study particularly adds to our understanding of current trends in the popularity of and attitude toward e-cigarettes among social media users. In addition, this study identified conflicting perceptions about e-cigarettes among social media users. This suggests that accurate and up-to-date information on the benefits and risks of e-cigarettes needs to be disseminated to current and potential e-cigarette users via social media platforms, which can serve as important educational channels. Future research can explore the efficacy of social media-based interventions that deliver appropriate information (eg, general facts, benefits, and risks) about e-cigarettes.Trial registrationPROSPERO CRD42019121611; https://tinyurl.com/yfr27uxs.

Project description:BACKGROUND:Seeking and sharing information are the primary uses of the internet and social media. It is therefore vital to understand the processes individuals go through when engaging with information on these diverse platforms, especially in areas such as health- and risk-related information. One important element of such engagement is evaluating and attributing expertise to others. OBJECTIVE:This study aimed to explore how meanings around expertise in relation to food allergy and intolerance (food hypersensitivity) were constructed by 2 groups of social media users: (1) those who use platforms for reasons relating to food hypersensitivity and (2) those seen as experts by this community. METHODS:Survey participants were asked open-ended questions to identify potential experts in food hypersensitivity issues on social media and to discuss their reasoning for their choices (n=143). Subsequently, 8 adult social media users with experience of managing food hypersensitivity and 5 participants designated as experts by those users took part in email interviews. Survey and interview data were analyzed thematically using Braun and Clarke's approach. RESULTS:Judging expertise on social media is a complex and multifaceted process. Users might be judged as experts through their professional background or their experience living with food hypersensitivities. How users behave on social media and the traces of their Web-based activity can influence how others will see them. Such considerations are both measured and moderated through the social media community itself. Findings highlighted how social media often act as a supportive information tool following a diagnosis, but this also raised concerns regarding the scenario of patients not being able to access suitable vetted information. CONCLUSIONS:This work has implications for understanding how users perceive expertise on social media in relation to a health concern and how information assessments are made during the management of risks. Findings provide practical insights to both medical and organizational stakeholders involved in the support of those living with life-changing conditions, such as food hypersensitivities.

Project description:BackgroundElectronic health records (EHRs) are a rich resource for developing applications to engage patients and foster patient activation, thus holding a strong potential to enhance patient-centered care. Studies have shown that providing patients with access to their own EHR notes may improve the understanding of their own clinical conditions and treatments, leading to improved health care outcomes. However, the highly technical language in EHR notes impedes patients' comprehension. Numerous studies have evaluated the difficulty of health-related text using readability formulas such as Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning-Fog Index (GFI). They conclude that the materials are often written at a grade level higher than common recommendations.ObjectiveThe objective of our study was to explore the relationship between the aforementioned readability formulas and the laypeople's perceived difficulty on 2 genres of text: general health information and EHR notes. We also validated the formulas' appropriateness and generalizability on predicting difficulty levels of highly complex technical documents.MethodsWe collected 140 Wikipedia articles on diabetes and 242 EHR notes with diabetes International Classification of Diseases, Ninth Revision code. We recruited 15 Amazon Mechanical Turk (AMT) users to rate difficulty levels of the documents. Correlations between laypeople's perceived difficulty levels and readability formula scores were measured, and their difference was tested. We also compared word usage and the impact of medical concepts of the 2 genres of text.ResultsThe distributions of both readability formulas' scores (P<.001) and laypeople's perceptions (P=.002) on the 2 genres were different. Correlations of readability predictions and laypeople's perceptions were weak. Furthermore, despite being graded at similar levels, documents of different genres were still perceived with different difficulty (P<.001). Word usage in the 2 related genres still differed significantly (P<.001).ConclusionsOur findings suggested that the readability formulas' predictions did not align with perceived difficulty in either text genre. The widely used readability formulas were highly correlated with each other but did not show adequate correlation with readers' perceived difficulty. Therefore, they were not appropriate to assess the readability of EHR notes.

Dataset Information

User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study.

Publications

User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets