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Annals of Behavioral Medicine
Published in: Annals of Behavioral Medicine 5/2016

01-10-2016 | Original Article

Health Communication in Social Media: Message Features Predicting User Engagement on Diabetes-Related Facebook Pages

Authors: Holly M. Rus, BA, Linda D. Cameron, PhD

Published in: Annals of Behavioral Medicine | Issue 5/2016

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Abstract

Background

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.

Purpose

This study analyzed health communications within ten diabetes-related Facebook pages to identify message features predictive of user engagement.

Method

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.

Results

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.

Conclusions

These findings hold promise for guiding communication design in health-related social media.
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Metadata
Title
Health Communication in Social Media: Message Features Predicting User Engagement on Diabetes-Related Facebook Pages
Authors
Holly M. Rus, BA
Linda D. Cameron, PhD
Publication date
01-10-2016
Publisher
Springer US
Published in
Annals of Behavioral Medicine / Issue 5/2016
Print ISSN: 0883-6612
Electronic ISSN: 1532-4796
DOI
https://doi.org/10.1007/s12160-016-9793-9

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