Real Men don’t say 'cute': Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes

Citation:
Carpenter, Jordan, Daniel Preoţiuc-Pietro, Lucie Flekova, Salvatore Giorgi, Courtney Hagan, Margaret Kern, Anneke Buffone, Lyle Ungar, and Martin Seligman. "Real Men don’t say 'cute': Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes." Social Psychological and Personality Science (2016).

Abstract:

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.

Related External Link

PreviewAttachmentSize
Draft4.31 MB
Supplemental Materials363.61 KB