Dataset Information

Influence of Flavors on the Propagation of E-Cigarette-Related Information: Social Media Study.

ABSTRACT: Modeling the influence of e-cigarette flavors on information propagation could provide quantitative policy decision support concerning smoking initiation and contagion, as well as e-cigarette regulations.The objective of this study was to characterize the influence of flavors on e-cigarette-related information propagation on social media.We collected a comprehensive dataset of e-cigarette-related discussions from public Pages on Facebook. We identified 11 categories of flavors based on commonly used categorizations. Each post's frequency of being shared served as a proxy measure of information propagation. We evaluated a set of regression models and chose the hurdle negative binomial model to characterize the influence of different flavors and nonflavor control variables on e-cigarette-related information propagation.We found that 5 flavors (sweet, dessert & bakery, fruits, herbs & spices, and tobacco) had significantly negative influences on e-cigarette-related information propagation, indicating the users' tendency not to share posts related to these flavors. We did not find a positive significance of any flavors, which is contradictory to previous research. In addition, we found that a set of nonflavor-related factors were associated with information propagation.Mentions of flavors in posts did not enhance the popularity of e-cigarette-related information. Certain flavors could even have reduced the popularity of information, indicating users' lack of interest in flavors. Promoting e-cigarette-related information with mention of flavors is not an effective marketing approach. This study implies the potential concern of users about flavorings and suggests a need to regulate the use of flavorings in e-cigarettes.

SUBMITTER: Zhou J

PROVIDER: S-EPMC5889495 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

Influence of Flavors on the Propagation of E-Cigarette-Related Information: Social Media Study.

Zhou Jiaqi J Zhang Qingpeng Q Zeng Daniel Dajun DD Tsui Kwok Leung KL

JMIR public health and surveillance 20180323 1

<h4>Background</h4>Modeling the influence of e-cigarette flavors on information propagation could provide quantitative policy decision support concerning smoking initiation and contagion, as well as e-cigarette regulations.<h4>Objective</h4>The objective of this study was to characterize the influence of flavors on e-cigarette-related information propagation on social media.<h4>Methods</h4>We collected a comprehensive dataset of e-cigarette-related discussions from public Pages on Facebook. We i ...[more]

PMID: 29572202

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Project description: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.

Project description:BACKGROUND:Although a number of studies have investigated how consumers use social media for health-related purposes, there is a paucity of studies in the Australian context. OBJECTIVE:This study aimed to explore how Australian consumers used social media for health-related purposes, specifically how they identified social media platforms, which were used, and which health-related activities commonly took place. METHODS:A total of 5 focus groups (n=36 participants), each lasting 60 to 90 minutes, were conducted in the Sydney metropolitan area. The group discussions were audiorecorded and transcribed verbatim. The transcripts were coded line-by-line and thematically analyzed. RESULTS:Participants used general search engines to locate health-related social media platforms. They accessed a wide range of social media on a daily basis, using several electronic devices (in particular, mobile phones). Although privacy was a concern, it did not prevent consumers from fully engaging in social media for health-related purposes. Blogs were used to learn from other people's experiences with the same condition. Facebook allowed consumers to follow health-related pages and to participate in disease-specific group discussions. Wikipedia was used for factual information about diseases and treatments. YouTube was accessed to learn about medical procedures such as surgery. No participant reported editing or contributing to Wikipedia or posting YouTube videos related to health topics. Twitter was rarely used for health-related purposes. Social media allowed consumers to obtain and provide disease and treatment-related information and social and emotional support for those living with the same condition. Most considered their participation as observational, but some also contributed (eg, responded to people's questions). CONCLUSIONS:Participants used a wide range of social media for health-related purposes. Medical information exchange (eg, disease and treatment) and social and emotional support were the cornerstones of their online activities. Social media appears to be used as a key tool to support disease self-management.

Project description:As 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.This 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.E-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.Clusters 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).Social 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.

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.

Dataset Information

Influence of Flavors on the Propagation of E-Cigarette-Related Information: Social Media Study.

Publications

Influence of Flavors on the Propagation of E-Cigarette-Related Information: Social Media Study.

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