Data_Sheet_1_Are You What You Read? Predicting Implicit Attitudes to Immigration Based on Linguistic Distributional Cues From Newspaper Readership; A Pre-registered Study.PDF
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https://figshare.com/articles/dataset/Data_Sheet_1_Are_You_What_You_Read_Predicting_Implicit_Attitudes_to_Immigration_Based_on_Linguistic_Distributional_Cues_From_Newspaper_Readership_A_Pre-registered_Study_PDF/8073860
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The implicit association test (IAT) measures bias towards often controversial topics (e.g., race, religion), while newspapers typically take strong positive/negative stances on such issues. In a pre-registered study, we developed and administered an immigration IAT to readers of the Daily Mail (a typically anti-immigration publication) and the Guardian (a typically pro-immigration publication) newspapers. IAT materials were constructed based on co-occurrence frequencies from each newspapers’ website for immigration-related terms (migrant/immigrant) and positive/negative attributes (skilled/unskilled). Target stimuli showed stronger negative associations with immigration concepts in the Daily Mail compared to the Guardian, and stronger positive associations in the Guardian corpus compared to the Daily Mail corpus. Consistent with these linguistic distributional differences, Daily Mail readers exhibited a larger IAT bias, revealing stronger negative associations to immigration concepts compared to Guardian readers. This difference in overall bias was not fully explained by other variables, and raises the possibility that exposure to biased language contributes to biased implicit attitudes.
内隐联想测验(Implicit Association Test,IAT)用于衡量个体针对常具争议性话题(如种族、宗教)的内隐偏见,而报纸通常会对这类议题采取鲜明的正向或负向立场。在一项预先注册的研究中,我们为《每日邮报》(Daily Mail,其立场通常偏向反移民)与《卫报》(Guardian,其立场通常偏向支持移民)的读者开发并施测了移民相关内隐联想测验。本次测验的材料基于两家报纸网站中与移民相关术语(移民者/移民)以及正负向属性(技能娴熟/技能匮乏)的共现频率构建。数据分析显示,相较于《卫报》语料库,《每日邮报》的目标刺激与移民概念呈现出更强的负向关联;而《卫报》语料库相较《每日邮报》则展现出更强的正向关联。与上述语言分布差异相一致,《每日邮报》的读者表现出更为显著的内隐联想测验偏见,相较《卫报》读者,他们对移民概念展现出更强烈的负向关联。这种整体偏见的差异无法通过其他变量得到完全解释,这提示接触偏见性语言可能会促成偏见性内隐态度的形成。
创建时间:
2019-05-03



