Hine Donald W, Phillips Keri L, Hine Michael J, Bhattacharya Oindrila, Phillips Wendy J, Driver Aaron B, Marks Anthony D G, Phillips Gary
School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, Canterbury, New Zealand.
School of Psychology, University of New England, Armidale, New South Wales, Australia.
PLoS One. 2025 Sep 10;20(9):e0331672. doi: 10.1371/journal.pone.0331672. eCollection 2025.
Effectively motivating public action on climate change remains a central challenge for science communicators. This study investigated how message and messenger attributes shape viewers' motivation to act on climate change, and whether these effects vary as a function of political orientation. Using a policy-capturing design, 581 U.S. adults each viewed six randomly selected short videos from the More than Scientists website, in which climate scientists described the personal relevance of climate change. Linguistic features of the messages were analyzed using the Linguistic Inquiry and Word Count (LIWC) software, and messenger attributes (e.g., age, sex, attractiveness) were independently coded. Multilevel modeling revealed that messenger characteristics-particularly being older, male, attractive, and filmed in natural settings-were the strongest predictors of viewer motivation, explaining over 21% of within-person variance. By contrast, linguistic message attributes had weak predictive power overall, though messages with future-focused language and greater length were modestly more motivating. Political orientation moderated some message effects: affiliation-oriented language increased motivation for left-leaning viewers, while achievement-oriented language was more effective for right-leaning viewers. These findings underscore the importance of peripheral cues in climate communication and support targeted messaging strategies that align with audience values and identities.
有效地激发公众应对气候变化的行动仍然是科学传播者面临的核心挑战。本研究调查了信息和传播者属性如何塑造观众应对气候变化采取行动的动机，以及这些影响是否因政治倾向而异。采用政策捕捉设计，581名美国成年人每人观看了从“超越科学家”网站随机选取的六个短视频，视频中气候科学家描述了气候变化的个人相关性。使用语言查询与字数统计（LIWC）软件分析信息的语言特征，并对传播者属性（如年龄、性别、吸引力）进行独立编码。多层次建模显示，传播者特征——特别是年龄较大、男性、有吸引力以及在自然环境中拍摄——是观众动机的最强预测因素，解释了超过21%的个体内部差异。相比之下，语言信息属性总体预测能力较弱，不过使用以未来为导向的语言且篇幅更长的信息在一定程度上更具激励性。政治倾向调节了一些信息效果：面向归属的语言增加了左倾观众的动机，而面向成就的语言对右倾观众更有效。这些发现强调了边缘线索在气候传播中的重要性，并支持与受众价值观和身份相契合的定向信息策略。
