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Stereotypical gender actions can be extracted from web text

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  • Amaç Herdağdelen
  • Marco Baroni

Abstract

We extracted gender‐specific actions from text corpora and Twitter, and compared them with stereotypical expectations of people. We used Open Mind Common Sense (OMCS), a common sense knowledge repository, to focus on actions that are pertinent to common sense and daily life of humans. We use the gender information of Twitter users and web‐corpus‐based pronoun/name gender heuristics to compute the gender bias of the actions. With high recall, we obtained a Spearman correlation of 0.47 between corpus‐based predictions and a human gold standard, and an area under the ROC curve of 0.76 when predicting the polarity of the gold standard. We conclude that it is feasible to use natural text (and a Twitter‐derived corpus in particular) in order to augment common sense repositories with the stereotypical gender expectations of actions. We also present a dataset of 441 common sense actions with human judges' ratings on whether the action is typically/slightly masculine/feminine (or neutral), and another larger dataset of 21,442 actions automatically rated by the methods we investigate in this study.

Suggested Citation

  • Amaç Herdağdelen & Marco Baroni, 2011. "Stereotypical gender actions can be extracted from web text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(9), pages 1741-1749, September.
    • Handle: RePEc:bla:jamist:v:62:y:2011:i:9:p:1741-1749
    DOI: 10.1002/asi.21579
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    Cited by:

    1. Rai, Rashmi & Rai, Ambarish Kumar, 2020. "Is sexual assault breaking women’s spatial confidence in cities of India? Some explorations from Varanasi city," Children and Youth Services Review, Elsevier, vol. 118(C).
    2. Vivek K. Singh & Mary Chayko & Raj Inamdar & Diana Floegel, 2020. "Female librarians and male computer programmers? Gender bias in occupational images on digital media platforms," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(11), pages 1281-1294, November.

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