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How to Improve Attitudes Toward Disliked Groups: The Effects of Narrative Versus Numerical Evidence on Political Persuasion

机译:如何提高对不喜欢的群体的态度:叙事与数字证据对政治说服力的影响

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We propose a model of how messages about groups one personally dislikes affect individual attitudes. We build upon theories of message persuasion and out-group acceptance to account for evidence type (numerical vs. narrative), facilitating conditions (encouraging empathy vs. objectivity), and the underlying mechanisms (immersion). We test this model in a pretest-posttest experiment, in which a sample of Americans (N = 601) read counter-attitudinal commentaries below articles presenting either narrative or numerical evidence about illegal immigrants or same-sex couples. Narratives led to greater message acceptance and greater immersion, especially in the empathetic condition. In turn, numerical messages led to self-perceived attitude change in the objective condition. Persuasive effects of narratives in the empathetic, but not the objective, condition were mediated by immersion.
机译:我们提出了一个模型,该模型关于个人不喜欢的群体的信息如何影响个人的态度。我们基于消息说服和群体接受的理论来解释证据类型(数字与叙述),便利条件(鼓励同理与客观)以及潜在的机制(沉浸)。我们在前测后测实验中对该模型进行了测试,其中一个美国人样本(N = 601)阅读以下文章中的反态度评论,这些文章提供了有关非法移民或同性伴侣的叙述或数字证据。叙事导致更大的消息接受度和更大的沉浸感,尤其是在移情条件下。反过来,数字消息导致在客观条件下自我感知的态度变化。叙事在说服力而非客观条件中的说服力是通过沉浸式介导的。

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