University of Kentucky, Lexington, KY, USA.
University of California, Irvine, CA, USA.
Risk Anal. 2018 Dec;38(12):2580-2598. doi: 10.1111/risa.13140. Epub 2018 Aug 6.
Social media platforms like Twitter and Facebook provide risk communicators with the opportunity to quickly reach their constituents at the time of an emerging infectious disease. On these platforms, messages gain exposure through message passing (called "sharing" on Facebook and "retweeting" on Twitter). This raises the question of how to optimize risk messages for diffusion across networks and, as a result, increase message exposure. In this study we add to this growing body of research by identifying message-level strategies to increase message passing during high-ambiguity events. In addition, we draw on the extended parallel process model to examine how threat and efficacy information influence the passing of Zika risk messages. In August 2016, we collected 1,409 Twitter messages about Zika sent by U.S. public health agencies' accounts. Using content analysis methods, we identified intrinsic message features and then analyzed the influence of those features, the account sending the message, the network surrounding the account, and the saliency of Zika as a topic, using negative binomial regression. The results suggest that severity and efficacy information increase how frequently messages get passed on to others. Drawing on the results of this study, previous research on message passing, and diffusion theories, we identify a framework for risk communication on social media. This framework includes four key variables that influence message passing and identifies a core set of message strategies, including message timing, to increase exposure to risk messages on social media during high-ambiguity events.
社交媒体平台(如 Twitter 和 Facebook)为风险沟通者提供了在新发传染病出现时及时接触目标人群的机会。在这些平台上,信息通过消息传递(在 Facebook 上称为“分享”,在 Twitter 上称为“转发”)获得曝光。这就提出了一个问题,即如何优化风险信息,以便在网络中传播,从而增加信息曝光度。在这项研究中,我们通过确定在高模糊性事件中增加消息传递的消息级别策略,为这一不断增长的研究领域做出了贡献。此外,我们借鉴扩展平行过程模型来检验威胁和效能信息如何影响寨卡风险信息的传递。2016 年 8 月,我们收集了美国公共卫生机构账户发布的 1409 条关于寨卡的 Twitter 消息。使用内容分析方法,我们确定了内在消息特征,然后使用负二项回归分析了这些特征、发送消息的账户、账户所在的网络以及寨卡作为话题的显著性对消息传递的影响。结果表明,严重性和效能信息增加了消息被他人转发的频率。根据这项研究的结果、以前关于消息传递的研究和扩散理论,我们确定了一个社交媒体风险沟通框架。该框架包括四个影响消息传递的关键变量,并确定了一组核心的消息策略,包括消息时机,以在高模糊性事件期间增加社交媒体上的风险信息曝光度。