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Related Experiment Video

Updated: May 10, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
LIWCbiased languageimplicit biaslanguagelinguistic category modellinguistic intergroup biasnatural language processingsocial biastext analysis

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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.