Health Communication in Social Media: Message Features Predicting User Engagement on Diabetes-Related Facebook Pages
- 1.3k Downloads
Social media provides unprecedented opportunities for enhancing health communication and health care, including self-management of chronic conditions such as diabetes. Creating messages that engage users is critical for enhancing message impact and dissemination.
This study analyzed health communications within ten diabetes-related Facebook pages to identify message features predictive of user engagement.
The Common-Sense Model of Illness Self-Regulation and established health communication techniques guided content analyses of 500 Facebook posts. Each post was coded for message features predicted to engage users and numbers of likes, shares, and comments during the week following posting.
Multi-level, negative binomial regressions revealed that specific features predicted different forms of engagement. Imagery emerged as a strong predictor; messages with images had higher rates of liking and sharing relative to messages without images. Diabetes consequence information and positive identity predicted higher sharing while negative affect, social support, and crowdsourcing predicted higher commenting. Negative affect, crowdsourcing, and use of external links predicted lower sharing while positive identity predicted lower commenting. The presence of imagery weakened or reversed the positive relationships of several message features with engagement. Diabetes control information and negative affect predicted more likes in text-only messages, but fewer likes when these messages included illustrative imagery. Similar patterns of imagery’s attenuating effects emerged for the positive relationships of consequence information, control information, and positive identity with shares and for positive relationships of negative affect and social support with comments.
These findings hold promise for guiding communication design in health-related social media.
KeywordsSocial media Health communication Facebook Diabetes Common-Sense Model
- 2.Fox S, Jones S. The Social Life of Health Information. Washington, DC: Pew Internet & American Life Project, 2009-12; 2009. Available at: http://www.pewinternet.org/2009/06/11/the-social-life-of-health-information/. Accessed January 25, 2014.Google Scholar
- 3.National Diabetes Information Clearinghouse. National Institute of Health Web site. http://www.diabetes.niddk.nih.gov/. Published 2012. Updated, March 30, 2013. Accessed April 13, 2013.Google Scholar
- 4.Facebook Company Information. Facebook Company Web site. http://newsroom.fb.com/company-info. Published 2015. Accessed March 22, 2016.Google Scholar
- 7.Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2014. Atlanta, GA: US Department of Health and Human Services; 2014.Google Scholar
- 8.Leventhal H, Brissette I, Leventhal EA. The Common-Sense Model of Self-Regulation of Health and Illness. In: Cameron LD, Leventhal H, eds. The Self-Regulation of Health and Illness Behaviour. London: Routledge; 2003: 42-65.Google Scholar
- 10.Cameron LD, Durazo A, Rus H. Illness representations. In: Benyamini Y, Johnston M, Karademas, V eds., Assessment in Health Psychology, in press.Google Scholar
- 11.Cameron LD, Moss-Morris R. Illness-Related Cognitions and Behaviour. In: French D, Vedhara K, Kaptein AA, Weinman JA, eds. Health Psychology. 2nd ed. Oxford: Blackwell; 2010: 84-110.Google Scholar
- 12.Leventhal H, Bodnar-Deren S, Breland JY, et al. Modeling Health and Illness Behavior: The Approach of the Common-Sense Model. In: Baum A, Revenson TA, Singer J, eds. Handbook of Health Psychology. 2nd ed. New York: Taylor & Francis; 2012: 585-606.Google Scholar
- 13.Paddison CAM, Alpass FM, Stephens CV. Using the common sense model of illness self-regulation to understand diabetes-related distress: The importance of being able to “make sense” of diabetes. NZ J Psychol. 2010; 39: 45-50.Google Scholar
- 17.Barnes L, Moss-Morris R, Kaufusi M. Illness beliefs and adherence in diabetes mellitus: Comparisons between Tongan and European patients. New Zeal Med J. 2004; 117: 1188.Google Scholar
- 18.Epstein S. Cognitive Experiential Self-Theory: An Integrative Theory of Personality. In: Tennen H, Suls J, eds. Handbook of Psychology, Vol. 5: Personality and Social Psychology. Hoboken, NJ: Wiley; 2003.Google Scholar
- 20.Magnan RE, Cameron LD. Do young adults perceive that graphic warnings provide new information about the harms of smoking? Ann Behav Med, 2015. 10.1007/s12160-015-9691-6.Google Scholar
- 25.Hoyt M, Stanton AL. Adjustment to Chronic Illness. In: Baum A, Revenson TA, Singer J, eds. Handbook of Health Psychology. 2nd ed. New York, NY: Taylor & Francis; 2012: 219-246.Google Scholar
- 26.Bender J, Jimenez-Marroquin MC, Jadad AR. Seeking support on Facebook: A content analysis of breast cancer groups. J Med Internet Res. 2011; 13. doi: 10.2196/jmir.1560.Google Scholar
- 28.Wiebe DJ, Berg C, Palmer D, et al. ‘Illness and the self: Examining adjustment among adolescents with diabetes,’ paper presented at the Annual Meeting of the Society of Behavioral Medicine. Washington DC. (2002).Google Scholar
- 29.Taylor SE, Aspinwall LG. Coping with Chronic Illness. In: Goldberger L, Breznitz S, eds. Handbook of Stress: Theoretical and Clinical Aspects. 2nd ed. New York, NY, US: Free Press; 1993: 511-531.Google Scholar
- 32.Muller C, Cameron LD. Trait anxiety, information modality, and responses to communications about prenatal genetic testing. J Behav Med. 2014; 1-12.Google Scholar
- 33.National Cancer Institute. Making Health Communication Programs Work: A Planner’s Guide. Rev. ed. Bethesda, MD: National Cancer Institute; 2001.Google Scholar
- 34.StataCorp. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP; 2013.Google Scholar
- 36.Raudenbush SW, Bryk AS. Hierarchical Linear Models: Applications and Data Analysis Methods. 2nd ed. Newbury Park, CA: Sage; 2002.Google Scholar
- 37.Hilbe, JM. Brief overview on interpreting count model risk ratios: An addendum to negative binomial regression; 2008.Google Scholar
- 39.Rothman AJ, Kelly KM, Hertel AW, Salovey P. Message Frames and Illness Representations: Implications for Interventions to Promote and Sustain Healthy Behavior. In: Cameron LD, Leventhal H, eds. The Self-Regulation of Health and Illness Behaviour. London: Routledge; 2003: 278-296.Google Scholar
- 40.Leventhal H, Leventhal E, Cameron LD. Representations, Procedures, and Affect in Illness Self Regulation: A Perceptual-Cognitive Approach. In: Baum A, Revenson T, Singer J, eds. Handbook of Health Psychology. New York: Erlbaum; 2001: 19-48.Google Scholar