Skip to main content
SpringerLink
Log in

Identifying Stereotypes in the Online Perception of Physical Attractiveness

  • Conference paper
  • First Online:
Social Informatics (SocInfo 2016)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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/.

References

  1. Cash, T.F., Brown, T.A.: Gender and body images: stereotypes and realities. Sex Roles 21(5), 361–373 (1989)

    Article  Google Scholar 

  2. Kay, M., Matuszek, C., Munson, S.A.: Unequal representation and gender stereotypes in image search results for occupations. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 3819–3828. ACM, New York (2015)

    Google Scholar 

  3. Downs, A.C., Harrison, S.K.: Embarrassing age spots or just plain ugly? Physical attractiveness stereotyping as an instrument of sexism on american television commercials. Sex Roles 13(1), 9–19 (1985)

    Article  Google Scholar 

  4. William, H.: The Analysis of Beauty: Written with a View of Fixing the Fluctuating Ideas of Taste. Samuel Bagster & Sons, London (1753)

    Google Scholar 

  5. Bakhshi, S., Shamma, D.A., Gilbert, E.: Faces engage us: photos with faces attract more likes and comments on instagram. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 965–974. ACM (2014)

    Google Scholar 

  6. Umoja Noble, S.: Google search: hyper-visibility as a means of rendering black women and girls invisible. InVis. Cult. J. Vis. Cult. Univ. Rochester. (2013). http://ivc.lib.rochester.edu/google-search-hyper-visibility-as-a-means-of-rendering-black-women-and-girls-invisible/

  7. Introna, L.D., Nissenbaum, H.: Shaping the web: why the politics of search engines matters. Inf. Soc. 16(3), 169–185 (2000)

    Article  Google Scholar 

  8. Sweeney, L.: Discrimination in online ad delivery. Queue 11(3), 10 (2013)

    Article  Google Scholar 

  9. Umoja Noble, S.: Missed connections: what search engines say about women (2012)

    Google Scholar 

  10. Mazza, F., Da Silva, M.P., Le Callet, P.: Racial identity and media orientation: exploring the nature of constraint. J. Black Stud. 29, 367–397 (1999)

    Article  Google Scholar 

  11. Allibhai, A.: On racial bias and the sharing economy (2016). https://goo.gl/mhpr6C. Accessed 13 May 2016

  12. Hoffman, K.M., Trawalter, S., Axt, J.R., Oliver, M.N.: Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proc. Nat. Acad. Sci. 113(16), 4296–4301 (2016). doi:10.1073/pnas.1516047113

    Article  Google Scholar 

  13. van den Berghe, P.L., Frost, P.: Skin color preference, sexual dimorphism and sexual selection: a case of gene culture co-evolution? Ethn. Racial Stud. 9(1), 87–113 (1986)

    Article  Google Scholar 

  14. Grammer, K., Fink, B., Moller, A.P., Thornhill, R.: Darwinian aesthetics: sexual selection and the biology of beauty. Biol. Rev. 78, 385–407 (2003)

    Article  Google Scholar 

  15. Fink, B., Grammer, K., Matts, P.J.: Visible skin color distribution plays a role in the perception of age, attractiveness, and health in female faces. Evol. Hum. Behav. 27(6), 433–442 (2006)

    Article  Google Scholar 

  16. Cunningham, M.R., Roberts, A.R., Barbee, A.P., Druen, P.B., Wu, C.H.: Their ideas of beauty are, on the whole, the same as ours. J. Pers. Soc. Psychol. 68, 261–279 (1995)

    Article  Google Scholar 

  17. Coetzee, V., Greeff, J.M., Stephen, I.D., Perrett, D.I.: Cross-cultural agreement in facial attractiveness preferences: the role of ethnicity and gender. PLoS ONE 9(7), 1–8 (2014)

    Article  Google Scholar 

  18. Eisenthal, Y., Dror, G., Ruppin, E.: Facial attractiveness: beauty and the machine. Neural Comput. 18(1), 119–142 (2006)

    Article  Google Scholar 

  19. Rationality: The Cambridge Dictionary of Philosophy, 2nd edn. Cambridge University Press (1999)

    Google Scholar 

  20. Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

  21. Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: Which algorithms implement ward’s criterion? J. Classif. 31(3), 274–295 (2014)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Camila Souza Araújo, Wagner Meira Jr. & Virgilio Almeida

Authors
  1. Camila Souza Araújo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wagner Meira Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Virgilio Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Camila Souza Araújo .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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.
Full size table
Table 5. Useful photos from Bing.
Full size table

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

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

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)
Full size table
Table 11. P-value table associated with the questions Q7 (Bing)
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47879-1

  • Online ISBN: 978-3-319-47880-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation