Can We Derive Explicit and Implicit Bias from Corpus?
This work addresses the need for automated methods to separate explicit and implicit bias in language analysis, which is useful for applications in psychology and social sciences, though it is incremental as it builds on existing psychological frameworks.
The paper tackles the problem of distinguishing explicit and implicit bias in language by developing an automatic technique that connects psychological measurements with sentiment analysis of statements containing specific word combinations, successfully reproducing small-scale psychological observations on large populations in English and Chinese media.
Language is a popular resource to mine speakers' attitude bias, supposing that speakers' statements represent their bias on concepts. However, psychology studies show that people's explicit bias in statements can be different from their implicit bias in mind. Although both explicit and implicit bias are useful for different applications, current automatic techniques do not distinguish them. Inspired by psychological measurements of explicit and implicit bias, we develop an automatic language-based technique to reproduce psychological measurements on large population. By connecting each psychological measurement with the statements containing the certain combination of special words, we derive explicit and implicit bias by understanding the sentiment of corresponding category of statements. Extensive experiments on English and Chinese serious media (Wikipedia) and non-serious media (social media) show that our method successfully reproduce the small-scale psychological observations on large population and achieve new findings.