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- W4323859461 abstract "Large-scale language datasets and advances in natural language processing offer new opportunities for studying people’s attitudes and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach doubles the accuracy rates achieved by existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures variance in implicit attitudes that is largely unexplained by explicit attitudes. Our results shed light on how implicit attitudes are represented and retrieved, and how they can be measured by combining standard psychological data with large-scale language data. In doing we pave the way for highly accurate computational modeling of attitudes and behaviors." @default.
- W4323859461 created "2023-03-11" @default.
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- W4323859461 date "2023-03-09" @default.
- W4323859461 modified "2023-09-29" @default.
- W4323859461 title "Predicting Implicit Attitudes with Natural Language Data" @default.
- W4323859461 doi "https://doi.org/10.31234/osf.io/gn6kw" @default.
- W4323859461 hasPublicationYear "2023" @default.
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