Are You What You Read? Predicting Implicit Attitudes to Immigration Based on Linguistic Distributional Cues From Newspaper Readership; A Pre-registered Study

摘要

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.