Real Men Don’t Say “Cute”
Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes
Bibliographic Data
People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the writer. Although raters often correctly categorize authors, they overestimate the importance of some stereotype-congruent signal. Findings suggest that data-driven approaches might be a valuable and ecologically valid tool for identifying even subtle aspects of stereotypes and highlighting the facets that are exaggerated or misapplied
Biology and political orientation · Categorization · Cognitive psychology · Linguistics · Perception · Politics · Social category · Social perception · Computational and Text Analysis Methods · Hate Speech and Cyberbullying Detection · Psychology · Social and Intergroup Psychology · Social Psychology
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Gender Differences in Language Use
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Accuracy and Bias in Stereotypes about the Social and Political Attitudes of Women and Men
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Automatic personality assessment through social media language
An individual and quantitative measure of stereotypes
Internal and external motivation to respond without prejudice
Male and female spoken language differences
Tests for comparing elements of a correlation matrix
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2018 - 2024 (7) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |