Beyond English: Considering Language and Culture in Psychological Text Analysis

Kučera, Dalibor; Mehl, Matthias R.

doi:10.3389/fpsyg.2022.819543

Cited by 6 publications

(10 citation statements)

References 129 publications

(168 reference statements)

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“…For most research in cognitive and social psychology, psycholinguistics, and cognitive science at large, text analysis has primarily focused on a very small and very specific part of human language, that of formal, written English from a WEIRD (Western, Educated, Industrialized, Rich and Democratic) population (Blasi et al, 2022;Henrich et al, 2010;Kučera & Mehl, 2022;Levisen, 2019). Most experiments conducted in these disciplines use English stimuli, and most linguistic analyses and computational linguistic tools are based on the English language.…”

Section: Introductionmentioning

confidence: 99%

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Linders,

Louwerse

2023

Behav Res

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Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer (https://lingualyzer.com), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface.

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Section: Introductionmentioning

confidence: 99%

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Linders,

Louwerse

2023

Behav Res

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“…Our work shows that the SCM subspace can be used to mitigate bias for additional social groups without lowering word embedding quality. Future directions of our work include: (1) using other social psychological frameworks for quantifying language-embedded social stereotypes (e.g., Koch et al, 2016); (2) extending these findings from English to other languages (Kučera and Mehl, 2022); and (3) the extension of our findings to other social group biases (e.g., nationality) (Herold et al, 2022) and intersectional biases (e.g., intersection of race and gender).…”

Section: Discussionmentioning

confidence: 98%

Social-Group-Agnostic Word Embedding Debiasing via the Stereotype Content Model

Omrani¹,

Kennedy²,

Atari³

et al. 2022

Preprint

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Existing word embedding debiasing methods require social-group-specific word pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which cannot be used to mitigate bias for other social groups, making these methods impractical or costly to incorporate understudied social groups in debiasing. We propose that the Stereotype Content Model (SCM), a theoretical framework developed in social psychology for understanding the content of stereotypes, which structures stereotype content along two psychological dimensions -"warmth" and "competence" -can help debiasing efforts to become social-group-agnostic by capturing the underlying connection between bias and stereotypes. Using only pairs of terms for warmth (e.g., "genuine"-"fake") and competence (e.g., "smart"-"stupid"), we perform debiasing with established methods and find that, across gender, race, and age, SCM-based debiasing performs comparably to group-specific debiasing.

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“…Moreover, languages vary widely in morphological structure and in the entities of a sentence that can be omitted (e.g., whether sentential subjects or objects can be dropped), which can significantly affect the number of words in an utterance across languages. When analyses have more complex aims (e.g., analysis of content, analysis of article use such as first-person pronouns), these challenges are amplified by other fundamental differences between languages, such as whether the language uses declensions, the availability and specificity of words conveying emotional tone, and norms (e.g., politeness) that may strongly govern word choice (Kučera & Mehl, 2022).…”

Section: Multilingual Coding and Transcriptionmentioning

confidence: 99%

Diversity, equity, and inclusivity in observational ambulatory assessment: Recommendations from two decades of Electronically Activated Recorder (EAR) research

Kaplan,

Tidwell,

Mehl

2023

Preprint

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Ambient audio sampling methods such as the Electronically Activated Recorder (EAR) have become increasingly prominent in clinical and social sciences research. These methods record snippets of naturalistically assessed audio from participants’ daily lives, enabling novel observational research about the daily social interactions, identities, environments, behaviors, and speech of populations of interest. In practice, these scientific opportunities are equaled by methodological challenges: researchers’ own cultural backgrounds and identities can easily and unknowingly permeate the collection, coding, analysis, and interpretation of social data from daily life. Ambient audio sampling poses unique and significant challenges to cultural humility, diversity, equity, and inclusivity (DEI) in scientific research that require systematized attention. Motivated by this observation, an international consortium of 21 researchers who have used ambient audio sampling methodologies created a workgroup with the aim of improving upon existing published guidelines. We pooled formally and informally documented challenges pertaining to DEI in ambient audio sampling from our collective experience on 40+ studies (most of which used the EAR app) in clinical and healthy populations ranging from children to older adults. This article presents our resultant recommendations and argues for the incorporation of community-engaged research methods in observational ambulatory assessment designs looking forward. We provide concrete recommendations across each stage typical of an ambient audio sampling study (recruiting and enrolling participants, developing coding systems, training coders, handling multi-linguistic participants, data analysis and interpretation, and dissemination of results) as well as guiding questions that can be used to adapt these recommendations to project-specific constraints and needs.

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Beyond English: Considering Language and Culture in Psychological Text Analysis

Cited by 6 publications

References 129 publications

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Social-Group-Agnostic Word Embedding Debiasing via the Stereotype Content Model

Diversity, equity, and inclusivity in observational ambulatory assessment: Recommendations from two decades of Electronically Activated Recorder (EAR) research

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