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An observational study of social and emotional support in smoking cessation Twitter accounts: content analysis of tweets
Quantitative Health Sciences Publications and Presentations
  • Mary Rocheleau, University of Massachusetts Medical School Worcester
  • Rajani S. Sadasivam, University of Massachusetts Medical School
  • Kate Baquis, University of Massachusetts Medical School Worcester
  • Hannah Stahl, University of Massachusetts Medical School Worcester
  • Rebecca L. Kinney, University of Massachusetts Medical School
  • Sherry L. Pagoto, University of Massachusetts Medical School
  • Thomas K. Houston, University of Massachusetts Medical School
UMMS Affiliation
Department of Quantitative Health Sciences; Department of Medicine, Division of Preventive and Behavioral Medicine; UMass Worcester Prevention Research Center
Date
1-14-2015
Document Type
Article
Abstract

BACKGROUND: Smoking continues to be the number one preventable cause of premature death in the United States. While evidence for the effectiveness of smoking cessation interventions has increased rapidly, questions remain on how to effectively disseminate these findings. Twitter, the second largest online social network, provides a natural way of disseminating information. Health communicators can use Twitter to inform smokers, provide social support, and attract them to other interventions. A key challenge for health researchers is how to frame their communications to maximize the engagement of smokers.

OBJECTIVE: Our aim was to examine current Twitter activity for smoking cessation.

METHODS: Active smoking cessation related Twitter accounts (N=18) were identified. Their 50 most recent tweets were content coded using a schema adapted from the Roter Interaction Analysis System (RIAS), a theory-based, validated coding method. Using negative binomial regression, the association of number of followers and frequency of individual tweet content at baseline was assessed. The difference in followership at 6 months (compared to baseline) to the frequency of tweet content was compared using linear regression. Both analyses were adjusted by account type (organizational or not organizational).

RESULTS: The 18 accounts had 60,609 followers at baseline and 68,167 at 6 months. A total of 24% of tweets were socioemotional support (mean 11.8, SD 9.8), 14% (mean 7, SD 8.4) were encouraging/engagement, and 62% (mean 31.2, SD 15.2) were informational. At baseline, higher frequency of socioemotional support and encouraging/engaging tweets was significantly associated with higher number of followers (socioemotional: incident rate ratio [IRR] 1.09, 95% CI 1.02-1.20; encouraging/engaging: IRR 1.06, 95% CI 1.00-1.12). Conversely, higher frequency of informational tweets was significantly associated with lower number of followers (IRR 0.95, 95% CI 0.92-0.98). At 6 months, for every increase by 1 in socioemotional tweets, the change in followership significantly increased by 43.94 (P = .027); the association was slightly attenuated after adjusting by account type and was not significant (P = .064).

CONCLUSIONS: Smoking cessation activity does exist on Twitter. Preliminary findings suggest that certain content strategies can be used to encourage followership, and this needs to be further investigated.

Rights and Permissions
Citation: J Med Internet Res. 2015 Jan 14;17(1):e18. doi: 10.2196/jmir.3768. Link to article on publisher's site
Related Resources
Link to Article in PubMed
Citation Information
Mary Rocheleau, Rajani S. Sadasivam, Kate Baquis, Hannah Stahl, et al.. "An observational study of social and emotional support in smoking cessation Twitter accounts: content analysis of tweets" Vol. 17 Iss. 1 (2015) ISSN: 1438-8871 (Linking)
Available at: http://works.bepress.com/rajani_sadasivam/34/