Associations between use of specific social media sites and electronic cigarette use among college students

Lin, Shuo-Yu; Cheng, Xiaolu; Rossheim, Matthew E.; Gress, Dustin A.; Cuellar, Alison Evans; Xue, Hong

doi:10.1080/07448481.2021.1965149

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…User engagement, defined as the number of visits to a website; ad click-through rates; and the number of shares, likes, and comments on a social media site are popular evaluation measures of proximal impact-behavioral intentions lead to changes in behavior-related beliefs, attitudes, and social expectations [22,23]. Changes in beliefs and expectations have been shown to lead to behavioral intentions and further cause behavioral changes [23][24][25][26]. As a result, tracking user engagement with antitobacco messages and content on social media platforms may be more effective as a way to influence people's attitudes and formulate behavioral intentions.…”

Section: Introductionmentioning

confidence: 99%

Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts

Lin¹,

Cheng²,

Zhang³

et al. 2023

J Med Internet Res

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Background Social media platforms provide a valuable source of public health information, as one-third of US adults seek specific health information online. Many antitobacco campaigns have recognized such trends among youth and have shifted their advertising time and effort toward digital platforms. Timely evidence is needed to inform the adaptation of antitobacco campaigns to changing social media platforms. Objective In this study, we conducted a content analysis of major antitobacco campaigns on Facebook using machine learning and natural language processing (NLP) methods, as well as a traditional approach, to investigate the factors that may influence effective antismoking information dissemination and user engagement. Methods We collected 3515 posts and 28,125 associated comments from 7 large national and local antitobacco campaigns on Facebook between 2018 and 2021, including the Real Cost, Truth, CDC Tobacco Free (formally known as Tips from Former Smokers, where “CDC” refers to the Centers for Disease Control and Prevention), the Tobacco Prevention Toolkit, Behind the Haze VA, the Campaign for Tobacco-Free Kids, and Smoke Free US campaigns. NLP methods were used for content analysis, including parsimonious rule–based models for sentiment analysis and topic modeling. Logistic regression models were fitted to examine the relationship of antismoking message-framing strategies and viewer responses and engagement. Results We found that large campaigns from government and nonprofit organizations had more user engagements compared to local and smaller campaigns. Facebook users were more likely to engage in negatively framed campaign posts. Negative posts tended to receive more negative comments (odds ratio [OR] 1.40, 95% CI 1.20-1.65). Positively framed posts generated more negative comments (OR 1.41, 95% CI 1.19-1.66) as well as positive comments (OR 1.29, 95% CI 1.13-1.48). Our content analysis and topic modeling uncovered that the most popular campaign posts tended to be informational (ie, providing new information), where the key phrases included talking about harmful chemicals (n=43, 43%) as well as the risk to pets (n=17, 17%). Conclusions Facebook users tend to engage more in antitobacco educational campaigns that are framed negatively. The most popular campaign posts are those providing new information, with key phrases and topics discussing harmful chemicals and risks of secondhand smoke for pets. Educational campaign designers can use such insights to increase the reach of antismoking campaigns and promote behavioral changes.

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Section: Introductionmentioning

confidence: 99%

Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts

Lin¹,

Cheng²,

Zhang³

et al. 2023

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

Social Media Data Mining of Anti-Tobacco Campaign Messages – A Facebook Study (Preprint)

Lin¹,

Cheng²,

Zhang³

et al. 2022

Preprint

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BACKGROUND Social media platforms provide a valuable source of public health information, as one-third of US adults seek specific health information online. Many anti-tobacco campaigns recognized such trends among youth and have shifted their advertising time and effort toward digital platforms. Timely evidence is needed to inform the adaptation of anti-tobaacco campaigns to changing social media platforms. OBJECTIVE In the present study, we conducted a content analysis of major anti-tobacco campaigns on Facebook using machine learning and natural language processing methods as well as a traditional approach to investigate the factors that may influence effective anti-smoking information dissemination and user engagement. METHODS We collected 3,515 posts and 28,125 associated comments from seven large national and local anti-tobacco campaigns on Facebook between 2018 and 2021 including The Real Cost, Truth, and CDC Tobacco Free (formally known as Tips from Former Smokers), Tobacco Prevention Toolkit, Behind the Haze VA, Campaign for Tobacco-Free Kids, and Smoke-Free US. Natural language processing methods were used for content analysis including parsimonious rule-based models for sentiment analysis and topic modeling. Logistic regression models were fitted to examine the relationship between anti-smoking message framing strategies and viewer responses and engagement. RESULTS We found that large campaigns from government and non-profit organizations had more user engagements compared to local and smaller campaigns. The Facebook users are more likely to engage in negatively-framed campaign posts. Negative posts tended to receive more negative comments (OR= 1.40, 95% CI 1.20 - 1.65). Positively framed posts generated more negative comments (OR = 1.41, 95% CI 1.19 - 1.66), as well as positive comments (OR = 1.29, 95% CI 1.13 - 1.48). Our content analysis and topic modeling uncovered that most popular campaign posts tended to be informational (i.e., providing new information), where the key phrases included talking about harmful chemicals (14.3%), as well as the risk to pets (6.3%). CONCLUSIONS Facebook users tended to engage with anti-tobacco educational campaigns more that are framed negatively. The most popular campaign posts are those providing new information, with key phrases and topics discussing harmful chemicals and risks of second-hand smoke for pets. Educational campaign designers can utilize such insights to increase the reach of anti-smoking campaigns and promote behavioral changes.

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Associations between use of specific social media sites and electronic cigarette use among college students

Cited by 2 publications

References 45 publications

Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts

Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts

Social Media Data Mining of Anti-Tobacco Campaign Messages – A Facebook Study (Preprint)

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