Young, lower-class, and algorithmically persuaded: exploring personalized advertising and its impact on social inequality
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Abstract
Algorithmically personalized advertising is a defining feature of the digital ecosystem, yet its potential to reinforce social inequalities remains underexplored. This study examines how socioeconomic status and gender intersect to influence exposure to personalized advertisements on platforms like Instagram and TikTok. Using online survey data from 1,200 participants aged 16 to 25, regression analyses reveal that young people from lower socioeconomic backgrounds are disproportionately targeted with ads promising easy income, quick earnings, and social mobility. They are also more likely to encounter ads related to gambling, online games, and financial services such as quick loans. These patterns are particularly pronounced among young men. Our findings highlight how social media platforms leverage massive data collection to infer sensitive attributes such as socioeconomic status, which includes complex information like education level, employment status, income, or migration background. This profiling exacerbates inequalities, as algorithmic advertising perpetuates both class-based barriers and gender stereotypes. Women are predominantly targeted with ads related to beauty, parenting, and education, while men are more exposed to ads for sports, alcohol, and politics. By applying an intersectional lens, this study underscores how algorithmic systems exploit vulnerabilities tied to overlapping identities, trapping the most vulnerable youth in a cycle of digital inequality. These findings emphasize the need for greater transparency in advertising algorithms and robust regulatory measures to ensure that personalization technologies foster equity rather than deepening societal divides.
References
Abbott, M. (2020). The changing epidemiology of gambling disorder and gambling-related harm: public health implications. Public Health, 184, 41-45. https://doi.org/10.1016/j.puhe.2020.04.003
Adams, J., Tyrrell, R., Adamson, A. J., & White, M. (2011). Socio-economic differences in exposure to television food advertisements in the UK: a cross-sectional study of advertisements broadcast in one television region. Public Health Nutrition, 15(3), 487-494. https://doi.org/10.1017/s1368980011001686
Adams, J., & White, M. (2009). Socio-economic and gender differences in nutritional content of foods advertised in popular UK weekly magazines. European Journal of Public Health, 19(2), 144-149. https://doi.org/10.1093/eurpub/ckn132
Agaku, I. T., Adisa, A. O., Akinyamoju, A. O., & Agboola, S. O. (2013). A cross-country comparison of the prevalence of exposure to tobacco advertisements among adolescents aged 13-15 years in 20 low and middle income countries. Tobacco Induced Diseases, 11(1), 11. https://doi.org/10.1186/1617-9625-11-11
Ali, M. (2021). Measuring and Mitigating Bias and Harm in Personalized Advertising. Fifteenth ACM Conference on Recommender Systems. https://doi.org/10.1145/3460231.3473895
Ali, M., Sapiezynski, P., Korolova, A., Mislove, A., & Rieke, A. (2019). Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging. WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, (pp. 13-21). https://doi.org/10.1145/3437963.3441801
Atkinson, A. M., Ross-Houle, K. M., Begley, E., & Sumnall, H. (2016). An exploration of alcohol advertising on social networking sites: an analysis of content, interactions and young people's perspectives. Addiction Research and Theory, 25(2), 91-102. https://doi.org/10.1080/16066359.2016.1202241
Backholer, K., Gupta, A., Zorbas, C., Bennett, R., Huse, O., Chung, A., Isaacs, A., Golds, G., Kelly, B., & Peeters, A. (2020). Differential exposure to, and potential impact of, unhealthy advertising to children by socio‐economic and ethnic groups: A systematic review of the evidence. Obesity Reviews, 22(3). https://doi.org/10.1111/obr.13144
Barone, C., Hertel, F. R., & Smallenbroek, O. (2022). The rise of income and the demise of class and social status? A systematic review of measures of socio-economic position in stratification research. Research In Social Stratification and Mobility, 78, 100678. https://doi.org/10.1016/j.rssm.2022.100678
Bol, N., Strycharz, J., Helberger, N., Van De Velde, B., & De Vreese, C. H. (2020). Vulnerability in a tracked society: Combining tracking and survey data to understand who gets targeted with what content. New Media & Society, 22(11), 1996-2017. https://doi.org/10.1177/1461444820924631
Buscemi, J., Acuff, S. F., Minhas, M., MacKillop, J., & Murphy, J. G. (2021). Identifying Patterns of Alcohol use and Obesity‐Related Factors among Emerging Adults: A Behavioral Economic analysis. Alcoholism/Alcoholism, Clinical and Experimental Research, 45(4), 828-840. https://doi.org/10.1111/acer.14569
Calo, R. (2014). Digital market manipulation. George Washington Law Review, 82(4), 995.
https://doi.org/10.2139/ssrn.2309703
Cassady, D. L., Liaw, K., & Miller, L. M. S. (2015). Disparities in Obesity-Related Outdoor advertising by neighborhood income and race. Journal of Urban Health, 92(5), 835-842. https://doi.org/10.1007/s11524-015-9980-1
Clario (2022) Big Brother Brands Report: Which Companies Access Our Personal Data the Most? Retrieved from https://clario.co/blog/which-company-uses-most-data/
World Inequality Lab (2022). World Inequality Report 2022. Retrieved from
Crenshaw, K. (1991). Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color. Stanford Law Review, 43(6), 1241. https://doi.org/10.2307/1229039 https://doi.org/10.2307/1229039
Dalenberg, D. J. (2018). Preventing discrimination in the automated targeting of job advertisements. Computer Law and Security Report/Computer Law & Security Report, 34(3), 615-627. https://doi.org/10.1016/j.clsr.2017.11.009
European Commission (2023). The Digital Services Act (DSA) explained: Measures to protect children and young people online. Retrieved from https://data.europa.eu/doi/10.2759/576008
Evans, M. D. R., & Kelley, J. (2004). Subjective social location: data from 21 nations. International Journal for Quality in Health Care, 16(1), 3-38. https://doi.org/10.1093/ijpor/16.1.3
Feijóo, B., & Sádaba, C. (2022). When mobile advertising is interesting: interaction of minors with ads and influencers' sponsored content on social media. Communication & Society, 35(3), 15-31. https://doi.org/10.15581/003.35.3.15-31
Friedman, S., & Laurison, D. (2020). The Class Ceiling: Why it Pays to be Privileged. Social Forces, 99(1), e5. https://doi.org/10.1093/sf/soz170
Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial intelligence in advertising: advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. SAGE Open, 13(4). https://doi.org/10.1177/21582440231210759
Global Witness (2021, September). How Facebook's ad targeting may be in breach of UK equality and data protection laws. Retrieved from
Global Witness (2023, June). New evidence of Facebook's sexist algorithm. Retrieved from https://www.globalwitness.org/en/campaigns/digital-threats/new-evidence-of-facebooks-sexist-algorithm/
Gómez, P., Feijóo, S., Braña, T., Varela, J., & Rial, A. (2020). Minors and online gambling: Prevalence and related variables. Journal of Gambling Studies, 36(3), 735-745. https://doi.org/10.1007/s10899-019-09923-3
Goodman, E., Adler, N. E., Kawachi, I., Frazier, A. L., Huang, B., & Colditz, G. A. (2001). Adolescents' perceptions of social status: Development and evaluation of a new indicator. Pediatrics, 108(2), e31. https://doi.org/10.1542/peds.108.2.e31 https://doi.org/10.1542/peds.108.2.e31
Gran, A., Booth, P., & Bucher, T. (2020). To be or not to be algorithm aware: a question of a new digital divide? Information, Communication & Society, 24(12), 1779-1796. https://doi.org/10.1080/1369118X.2020.1736124
Hartmann, B., Hudders, L., & De Jans, S. (2024). Exploring the Impact of Gambling Advertising on Children: A Comprehensive Review: A Critical Attitude to Embedded Formats. Advertising Literacy for Young Audiences in the Digital Age (pp. 47-63). https://doi.org/10.1007/978-3-031-55736-1_4
IAB. (2024, April). IAB/PWC Internet Advertising Revenue Report 2024. Retrieved from https://www.iab.com/insights/internet-advertising-revenue-report-2024/
Idescat (2021, April). Índex socioeconòmic territorial. Retrieved from https://www.idescat.cat/pub/?id=ist
Instagram (n.d.). Why am I seeing ads from an advertiser on Instagram? Retrieved from https://help.instagram.com/609473930427331?cms_id=609473930427331
INE (2022, November) Demografía y población. Cifras de población y censos demográficos. Retrieved from https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176951&menu=ultiDatos&idp=1254735572981
INE (2023). Uso de productos TIC por las personas de 16 a 74 años. Retrieved from https://www.ine.es/jaxi/Tabla.htm?tpx=55071&L=0
INE (2024, February). Mujeres y hombres en España: Determinantes de salud. Retrieved from https://www.ine.es/uc/zujWXsQLi1
Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising. Journal of Advertising Research, 58(3), 263-267. https://doi.org/10.2501/jar-2018-035 https://doi.org/10.2501/JAR-2018-035
Lambrecht, A., & Tucker, C. (2019). Algorithmic Bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966-2981. https://doi.org/10.1287/mnsc.2018.3093
Li, H. (2019). Special section Introduction: Artificial Intelligence and Advertising. Journal of Advertising, 48(4), 333-337. https://doi.org/10.1080/00913367.2019.1654947 https://doi.org/10.1080/00913367.2019.1654947
Molinaro, S., Benedetti, E., Scalese, M., Bastiani, L., Fortunato, L., Cerrai, S., Canale, N., Chomynova, P., Elekes, Z., Feijão, F., Fotiou, A., Kokkevi, A., Kraus, L., Rupšienė, L., Monshouwer, K., Nociar, A., Strizek, J., & Lazar, T. U. (2018). Prevalence of youth gambling and potential influence of substance use and other risk factors throughout 33 European countries: first results from the 2015 ESPAD study. Addiction, 113(10), 1862-1873. https://doi.org/10.1111/add.14275
OEDA (2023). Alcohol, tabaco y drogas ilegales en España. Retrieved from https://pnsd.sanidad.gob.es/profesionales/sistemasInformacion/informesEstadisticas
Olsen, J. R., Patterson, C., Caryl, F. M., Robertson, T., Mooney, S. J., Rundle, A. G., Mitchell, R., & Hilton, S. (2021). Exposure to unhealthy product advertising: Spatial proximity analysis to schools and socio-economic inequalities in daily exposure measured using Scottish children's individual-level GPS data. Health & Place, 68, 102535. https://doi.org/10.1016/j.healthplace.2021.102535
Pisarska, A., & Ostaszewski, K. (2020). Factors associated with youth gambling: longitudinal study among high school students. Public Health, 184, 33-40. https://doi.org/10.1016/j.puhe.2020.03.017
Powell, L. M., Szczypka, G., Chaloupka, F. J., & Braunschweig, C. L. (2007). Nutritional content of television food advertisements seen by children and adolescents in the United States. Pediatrics, 120(3), 576-583. https://doi.org/10.1542/peds.2006-3595
TikTok (n.d.) Ads and your Data. Retrieved from https://www.tiktok.com/privacy/ads-and-your-data/en
Radesky, J., Chassiakos, Y. R., Ameenuddin, N., & Navsaria, D. (2020). Digital advertising to children. Pediatrics, 146(1), e20201681. https://doi.org/10.1542/peds.2020-1681
Ragnedda, M. (2020). Enhancing digital equity: Connecting the Digital Underclass. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-49079-9
Ragnedda, M., Ruiu, M. L., & Addeo, F. (2022). The self-reinforcing effect of digital and social exclusion: The inequality loop. Telematics and Informatics, 72, 101852. https://doi.org/10.1016/j.tele.2022.101852
Rathee, S., Banker, S., Mishra, A., & Mishra, H. (2023). Algorithms propagate gender bias in the marketplace-with consumers' cooperation. Journal of Consumer Psychology, 33(4), 621-631. https://doi.org/10.1002/jcpy.1351
Rodgers, W., & Nguyen, T. (2022). Advertising benefits from ethical artificial intelligence algorithmic purchase decision pathways. Journal of Business Ethics, 178(4), 1043-1061. https://doi.org/10.1007/s10551-022-05048-7
Sáez-Linero, C., Rodríguez-de-Dios, I., & Jiménez-Morales, M. (2025). Swipe or subscribe: Do young people really prefer an ad-free Instagram? Index.Comunicación, 15(1), 125-151. https://doi.org/10.62008/ixc/15/01Pasaro
Singh, V., Chayko, M., Inamdar, R., & Floegel, D. (2020). Female librarians and male computer programmers? Gender bias in occupational images on digital media platforms. Journal of the Association for Information Science and Technology, 71(11), 1281-1294. https://doi.org/10.1002/asi.24335
Suárez-Álvarez, R., & Pastor-Rodríguez, A. (2023). Influencer advertising on TikTok: advert formats and illicit product advertising. A study involving Germany, France, Spain and Italy. Communication & Society, 36(3), 175-191. https://doi.org/10.15581/003.36.3.175-191 https://doi.org/10.15581/003.36.3.175-191
WHO (2024, May). Restricting digital marketing in the context of tobacco, alcohol, food and beverages, and breast-milk substitutes: existing approaches and policy options. Retrieved from https://www.who.int/publications/i/item/97892400772490
WHO (2018, June). Monitoring and restricting digital marketing of unhealthy products to children and adolescents: report based on the expert meeting on monitoring of digital marketing of unhealthy products to children and adolescents. Retrieved from https://iris.who.int/handle/10665/346585
Wu, L., & Wen, T. J. (2021). Understanding AI advertising from the consumer perspective. Journal of Advertising Research, 61(2), 133-146. https://doi.org/10.2501/jar-2021-004
Zarouali, B., Boerman, S. C., Voorveld, H. A., & Van Noort, G. (2022). The algorithmic persuasion framework in online communication: conceptualization and a future research agenda. Internet Research, 32(4), 1076-1096. https://doi.org/10.1108/intr-01-2021-0049
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