Abstract
Lack of diversity in advertising is a long-standing problem. Despite growing cultural awareness and missed business opportunities, many minorities remain under- or inappropriately represented in advertising. Previous research has studied how people react to culturally embedded ads, but such work focused mostly on print media or television using lab experiments. In this work, we look at diversity in content posted by 69 U.S. brands on two social media platforms, Instagram and Facebook. Using face detection technology, we infer the gender, race, and age of both the faces in the ads and of the users engaging with ads. Using this dataset, we investigate the following: (1) What type of content brands put out – Is there a lack of diversity?; (2) How does a brand’s content diversity compare to its audience diversity – Is any lack of diversity simply a reflection of the audience?; and (3) How does brand diversity relate to user engagement – Do users of a particular demographic engage more if their demographics are represented in a post?
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References
An, J., Weber, I.: #greysanatomy vs. #yankees: demographics and hashtag use on Twitter. In: ICWSM (2016)
Appiah, O.: Black, White, Hispanic, and Asian American adolescents’ responses to culturally embedded ads. Howard J. Commun. 12(1), 29–48 (2001)
Bakhshi, S., Shamma, D.A., Gilbert, E.: Faces engage us: photos with faces attract more likes and comments on instagram. In: CHI (2014)
Brinker, J.T., Schadendorf, D., Klode, J., Cosgarea, I., Rösch, A., Jansen, P., Stoffels, I., Izar, B.: Photoaging mobile apps as a novel opportunity for melanoma prevention: pilot study. JMIR Mhealth Uhealth 5(7), e101 (2017)
Bush, R.F.: White consumer sales response to black models. J. Mark. 38(2), 25–29 (1974)
Cagley, J.W., Cardozo, R.N.: White response to integrated advertising. J. Advert. Res. 10(2), 35–39 (1970)
Estes, A.C.: Brief History Racist Soft Drinks (2013). https://goo.gl/XvYGD7
Forehand, M.R., Deshpandé, R., Reed II, A.: Identity salience and the influence of differential activation of the social self-schema on advertising response. J. Appl. Psychol. 87(6), 1086–1099 (2002)
Garcia, D., Weber, I., Garimella, V.R.K.: Gender asymmetries in reality and fiction: the Bechdel test of social media. In: ICWSM (2014)
Henderson, J.J., Baldasty, G.J.: Race, advertising, and prime-time television. Howard J. Commun. 14(2), 97–112 (2003)
Hershfield, H.E., et al.: Increasing saving behavior through age-progressed renderings of the future self. J. Mark. Res. 48, S23–S37 (2011)
Jung, S., An, J., Kwak, H., Salminen, J., Jansen, B.: Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race. In: ICWSM (2018)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: ICLR (2018)
Kocabey, E., et al.: Face-to-BMI: using computer vision to infer body mass index on social media. In: ICWSM (2017)
Kocabey, E., Ofli, F., Marin, J., Torralba, A., Weber, I.: Using computer vision to study the effects of BMI on online popularity and weight-based homophily. In: SocInfo (2018)
Lee, Y.J., Kim, S.: How do racial minority consumers process a model race cue in CSR advertising? A comparison of Asian and White Americans. J. Mark. Commun. 1–21 (2017)
Lloyds: Lloyds Diversity Report (2016). https://goo.gl/ehck2D
Martin, B.: The influence of ad model ethnicity and self-referencing on attitudes: evidence from New Zealand. J. Advert. 33(4), 27–37 (2004)
Miller, A.N., Kinya, J., Booker, N., Kizito, M., wa Ngula, K.: Kenyan patients attitudes regarding doctor ethnicity and doctor-patient ethnic discordance. Patient Educ. Couns. 82(2), 201–206 (2011)
NPR: This Ad’s For You (2015). https://goo.gl/z8jQTB
NYTimes: Upbeat Interracial Ad for Old Navy Leads to Backlash. Twice (2016). https://goo.gl/RHkjsS
Olivola, C.Y., Todorov, A.: Elected in 100 milliseconds: appearance-based trait inferences and voting. J. Nonverbal Behav. 34(2), 83–110 (2010)
Paek, H., Shah, H.: Racial ideology, model minorities, and the “not-so-silent partner:” stereotyping of Asian Americans in U.S. magazine advertising. Howard J. Commun. 14(4), 225–243 (2003)
Qualls, W.J., Moore, D.J.: Stereotyping effects on consumers’ evaluation of advertising: Impact of racial differences between actors and viewers. Psychol. Mark. 7(2), 135–151 (1990)
Reis, J., Kwak, H., An, J., Messias, J., Benevenuto, F.: Demographics of news sharing in the U.S. Twittersphere. In: HT (2017)
Sierra, J.J., Hyman, M.R., Torres, I.M.: Using a model’s apparent ethnicity to influence viewer responses to print ads: a social identity theory perspective. J. Curr. Issues Res. Advert. 31(2), 41–66 (2009)
Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15(1), 72–101 (1904)
Travassos, C., Williams, D.R.: The concept and measurement of race and their relationship to public health: a review focused on Brazil and the United States. Cadernos de Saúde Pública 20, 660–678 (2004)
Tsai, W.H.S.: Assimilating the queers: representations of lesbians, gay men, bisexual, and transgender people in mainstream advertising. Advert. Soc. Rev. 11(1) (2010)
Zagheni, E., Garimella, V.R.K., Weber, I.: Inferring international and internal migration patterns from twitter data. In: WWW (2014)
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An, J., Weber, I. (2018). Diversity in Online Advertising: A Case Study of 69 Brands on Social Media. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11185. Springer, Cham. https://doi.org/10.1007/978-3-030-01129-1_3
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