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Automating the Detection of Linguistic Intergroup Bias Through Computerized Language Analysis
Katherine A Collins1, Ryan L Boyd2
1University of Saskatchewan, Saskatoon, SK, Canada.
Journal of Language and Social Psychology
|April 28, 2025
Summary
This study introduces an automated method to detect linguistic bias, specifically Linguistic Intergroup Bias (LIB). Automated coding using sentiment analysis and abstraction provides a promising alternative to manual analysis.
Area of Science:
- Psycholinguistics
- Computational Linguistics
- Social Psychology
Background:
- Linguistic bias involves differential abstraction for behaviors across groups.
- The Linguistic Category Model (LCM) defines a concrete-to-abstract word continuum.
- Linguistic Intergroup Bias (LIB) reflects in abstract/concrete word use for ingroup/outgroup behaviors.
Purpose of the Study:
- To develop an automated method for coding Linguistic Intergroup Bias (LIB).
- To overcome the limitations of time-consuming manual coding in LIB research.
Main Methods:
- Utilized sentence tokenization, sentiment analysis, and abstraction coding.
- Employed automated approaches including CoreNLP sentiment analysis and LCM Dictionary abstraction coding.
Main Results:
- Automated coding methods produced scores approximating manual coding.
- This suggests that complex methods for LIB coding may not be necessary.
Conclusions:
- Automated approaches are effective for coding Linguistic Intergroup Bias (LIB).
- Recommends using CoreNLP sentiment analysis and LCM Dictionary abstraction coding for LIB detection.
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