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Identifying Stereotypes in the Online Perception of Physical Attractiveness

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10046)

Abstract

Stereotyping can be viewed as oversimplified ideas about social groups. They can be positive, neutral or negative. The main goal of this paper is to identify stereotypes for female physical attractiveness in images available in the Web. We look at the search engines as possible sources of stereotypes. We conducted experiments on Google and Bing by querying the search engines for beautiful and ugly women. We then collect images and extract information of faces. We propose a methodology and apply it to analyze photos gathered from search engines to understand how race and age manifest in the observed stereotypes and how they vary according to countries and regions. Our findings demonstrate the existence of stereotypes for female physical attractiveness, in particular negative stereotypes about black women and positive stereotypes about white women in terms of beauty. We also found negative stereotypes associated with older women in terms of physical attractiveness. Finally, we have identified patterns of stereotypes that are common to groups of countries.

Keywords

  • Discrimination
  • Algorithm bias
  • Beauty stereotypes

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Notes

  1. 1.

    http://www.faceplusplus.com/.

  2. 2.

    Using Google Translator: http://translate.google.com.br/.

  3. 3.

    R library: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/hclust.html.

  4. 4.

    http://www.indexmundi.com/japan/demographics_profile.html.

  5. 5.

    http://www.indexmundi.com/argentina/ethnic_groups.html.

  6. 6.

    http://www.southafrica.info/about/people/population.htm#.V4koMR9yvCI.

  7. 7.

    http://kff.org/other/state-indicator/distribution-by-raceethnicity/.

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Acknowledgments

This work was partially funded by Fapemig, CNPq, CAPES, and by projects InWeb, MASWeb, and EUBra-BIGSEA.

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Correspondence to Camila Souza Araújo .

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Appendices

A Data Gathering Statistics

Tables 4 and 5 present the number of photos that Face++ was able to detect a single face per country and for Google and Bing, respectively.

Table 4. Useful photos from Google.
Table 5. Useful photos from Bing.

B Results of Z-score Tests

In the Tables 6 and 7 the results highlighted are those which we reject the null hypothesis and accept the alternative hypothesis. In other words, we can answer YES to the questions Q1, Q2 and/or Q3.

Table 6. Z-score table associated with the questions Q1, Q2 and Q3 (Google)
Table 7. Z-score table associated with the questions Q1, Q2 and Q3 (Bing)

In the Tables 8 and 9 the results highlighted are those which we keep the alternative hypothesis and we can answer YES to the questions Q4, Q5 and/or Q6.

Table 8. Z-score table associated with the questions Q4, Q5 and Q6 (Google)
Table 9. Z-score table associated with the questions Q4, Q5 and Q6 (Bing)

C Results of Wilcoxon tests

Results highlighted in the Tables 10 and 11 show those countries for which we keep the alternative hypothesis.

Table 10. P-value table associated with the questions Q7 (Google)
Table 11. P-value table associated with the questions Q7 (Bing)

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Araújo, C.S., Meira, W., Almeida, V. (2016). Identifying Stereotypes in the Online Perception of Physical Attractiveness. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-47880-7_26

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