Applying linguistic methods to understanding smoking-related conversations on Twitter. Issue 2 (13th November 2013)

• Record Type: Journal Article
• Authors: Sanders-Jackson, Ashley; Brown, Cati G; Prochaska, Judith J
• Is Part Of: Tobacco control. Volume 24:Issue 2(2015)
• Page Start: 136
• Page End: 138
• Publication Date: 2013-11-13
• Subjects: Social marketing -- Prevention -- Advertising and Promotion -- Addiction; Tobacco use -- Prevention -- Periodicals; Tobacco use -- Periodicals; Smoking -- Law and legislation -- Periodicals; Smoking -- prevention & control -- Periodicals; Tobacco Use Disorder -- prevention & control -- Periodicals; Tobacco -- Periodicals; Electronic journals; 613.85
• DOI: 10.1136/tobaccocontrol-2013-051243
• Languages: English
• ISSNs: 0964-4563

Abstract

Abstract : Introduction: Social media, such as Twitter, have become major channels of communication and commentary on popular culture, including conversations on our nation's leading addiction: tobacco. The current study examined Twitter conversations following two tobacco-related events in the media: (1) President Obama's doctor announcing that he had quit smoking and (2) the release of a photograph of Miley Cyrus (a former Disney child star) smoking a cigarette. With a focus on high-profile individuals whose actions can draw public attention, we aimed to characterise tobacco-related conversations as an example of tobacco-related public discourse and to present a novel methodology for studying social media. Methods: Tweets were collected 11–13 November 2011 (President Obama) and 1–3 August 2011 (Miley Cyrus) and analysed for relative frequency of terms, a novel application of a linguistic methodology. Results: The President Obama data set (N=2749 tweets) had conversations about him quitting tobacco as well as a preponderance of information on political activity, links to websites, racialised terms and mention of marijuana. Websites and terms about Obama's smoke-free status were most central to the conversation. In the Miley Cyrus data (N=4746 tweets), terms that occurred with the greatest relative frequency were positive, emotional and supportive of quitting (eg, love, and please), with words such as 'love' most central to the conversation. Conclusions: People are talking about tobacco-related issues on Twitter, and semantic network analysis can be used to characterise on-line conversations. Future interventions may be able to harness social media and major current events to raise awareness of smoking-related issues.