Rus Holly M, Cameron Linda D
Department of Psychological Sciences, University of California, Merced, 5200 N. Lake Road, Merced, CA, 95343, USA.
Ann Behav Med. 2016 Oct;50(5):678-689. doi: 10.1007/s12160-016-9793-9.
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.
社交媒体为加强健康传播和医疗保健提供了前所未有的机遇，包括糖尿病等慢性病的自我管理。创作能吸引用户的信息对于增强信息影响力和传播至关重要。
本研究分析了十个与糖尿病相关的脸书页面内的健康传播情况，以确定预测用户参与度的信息特征。
疾病自我调节的常识模型和既定的健康传播技巧指导了对500条脸书帖子的内容分析。每条帖子都针对预计能吸引用户的信息特征以及发布后一周内的点赞、分享和评论数量进行编码。
多层次负二项回归显示，特定特征预测了不同形式的参与度。图片成为一个强有力的预测因素；有图片的信息相对于无图片的信息有更高的点赞和分享率。糖尿病后果信息和积极身份预测了更高的分享率，而消极情绪、社会支持和众包预测了更高的评论率。消极情绪、众包和外部链接的使用预测了更低的分享率，而积极身份预测了更低的评论率。图片的存在削弱或逆转了几个信息特征与参与度之间的正向关系。糖尿病控制信息和消极情绪在纯文本信息中预测了更多的点赞，但当这些信息包含说明性图片时点赞较少。图片的削弱效应在后果信息、控制信息和积极身份与分享的正向关系以及消极情绪和社会支持与评论的正向关系中也呈现出类似模式。
这些发现有望指导健康相关社交媒体中的传播设计。
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