Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions

Abstract

ObjectiveTobacco 21 is a law that sets the minimum legal sales age of tobacco products to 21. On 20 December 2019, the USA passed a federal Tobacco 21 law. The objective of this study is to explore Twitter discussions about the federal Tobacco 21 law in the USA leading up to enacted.MethodsTwitter messages about Tobacco 21 posted between September and December 2019 were collected via RITHM software. A 2% sample of all collected tweets were double coded by independent coders using a content analysis approach.ResultsFindings included three content categories of tweets (news, youth and young adults and methods of avoiding the law) with eight subcodes. Most news tweets incorrectly described the law as a purchase law (54.7%). However, Tobacco 21 is in fact a sales law—it only includes penalties for tobacco retailers who sell to under-age purchasers. About one-fourth (27%) of the tweets involved youth and young adults, with some claiming the law would reduce youth smoking and others doubting its ability to limit youth access to tobacco products. Few tweets (2.5%) mentioned methods of circumventing the policy, such as having an older peer purchase tobacco.ConclusionsAs several countries explore raising their age of sale of tobacco laws to 21, they should couple policy enactment with clear and accurate communication about the law. Compliance agencies at all levels (eg, local, regional, national) can use social media to identify policy loopholes and support vulnerable populations throughout the policy implementation process.

The Experiences of Stakeholders Using Social Media as a Tool for Health Service Design and Quality Improvement: A Scoping Review

“…Improves the quality of information gathered or shared. Social media was seen to improve the quality of information gathered or shared by organisations [ 46 , 47 , 48 , 49 , 54 , 63 , 65 , 66 , 68 , 69 , 73 , 75 , 76 , 84 , 85 , 86 , 90 , 95 ]. The information gathered from social media relating to QI and service design was viewed as richer and more authentic than information gathered through other methods [ 46 , 49 , 66 , 68 , 69 , 73 , 85 ], and social media was perceived to be a good source of additional qualitative information which added context to quantitative performance data [ 65 ].…”

“…The information gathered from social media relating to QI and service design was viewed as richer and more authentic than information gathered through other methods [ 46 , 49 , 66 , 68 , 69 , 73 , 85 ], and social media was perceived to be a good source of additional qualitative information which added context to quantitative performance data [ 65 ]. In the context of health policy development, social media was viewed as a good tool for gathering a broad understanding of issues impacting health policy development or change [ 47 , 48 , 56 , 64 , 75 , 84 , 90 , 95 ].…”

“…The most frequently reported risk or limitation theme was that social media is underutilised by target audiences of the health service or QI/design activity [ 42 , 45 , 47 , 51 , 52 , 56 , 59 , 60 , 63 , 64 , 65 , 66 , 70 , 74 , 75 , 76 , 78 , 79 , 83 , 84 , 86 , 89 , 90 , 96 , 97 ]. There was a belief the people who used social media were not representative of the stakeholders who would be affected by the change in service design or health policy [ 42 , 47 , 51 , 52 , 56 , 59 , 60 , 63 , 64 , 66 , 70 , 75 , 76 , 83 , 84 , 86 , 90 , 97 ] and that few people in general used social media [ 51 , 52 , 65 , 74 ]. Particular target groups reported to be less likely to use social media were people over 40 years old [ 79 , 96 ,…”

“…Malicious, fake or negative messages and actions were recognised as a risk [ 41 , 42 , 47 , 49 , 51 , 52 , 53 , 64 , 74 , 84 , 86 , 90 , 97 ]. Negative, false or unsavoury messages (that are unable to be removed on some platforms) [ 42 , 51 , 52 , 53 , 74 , 86 , 90 , 97 ] and users misrepresenting themselves [ 53 ] were examples of malicious online behaviour from the included studies.…”

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series

“…This was crucial to our analysis as it prevented the factor of tweets or retweets with the same text from influencing our findings. We used search terms that capture Twitter chatter related to e-cigarettes, similar to past research [28][29][30], including words such as vape, vapes, vaper, vapers, vaping, JUUL, JUULs, JUULing, and tobacco. A total of 1% (n=2401) of the tweets posted between August 23, 2019, and September 25, 2019, were selected for annotation by 2 independent researchers.…”