Exploring nexus of social media algorithms, content creators, and gender bias: A systematic literature review
List of Authors
  • Nor Azura Adzharuddin , Shijun Lou

Keyword
  • Social Media, Algorithm, Gender Bias, Gender Stereotype, Content Creators

Abstract
  • Drawing on the PRISMA framework, this study systematically investigates the dynamics between social media algorithms, content creators, and gender bias. An analysis of 18 quantitative and mixed-method studies from the Web of Science and Scopus databases, spanning 2019 to 2023, uncovers three main research trajectories: algorithms' influence on gender bias, their role in shaping content, and the interactions between algorithms, gender bias, and content creators. The review synthesizes diverse theoretical approaches and models, offering comprehensive insights into the complex nexus of algorithms, gender bias, and content creators. The application of varied research methodologies, including experiments, surveys, and content analyses, facilitates a thorough examination of algorithmic impacts. The chosen studies, focusing on different social media platforms and algorithmic features, reflect the varied interests of researchers. The findings reveal that algorithms perpetuate gender stereotypes by processing and learning content imbued with gender biases and further marginalizing gender minorities, reinforcing binary gender norms. The algorithmic curation of popular content also introduces inequities among content creators. Highlighting the need for equitable and inclusive digital environments, this review advocates for ethical content creation and algorithmic practices to mitigate gender bias and foster equality on social media platforms.

Reference
  • 1. Abul-Fottouh, D., Song, M. Y., & Gruzd, A. (2020, August). Examining algorithmic biases in YouTube’s recommendations of vaccine videos. International Journal of Medical Informatics, 140, 104175. https://doi.org/10.1016/j.ijmedinf.2020.104175 2. Adá Lameiras, A., & Rodríguez-Castro, Y. (2020, February 26). The presence of female athletes and non-athletes on sports media Twitter. Feminist Media Studies, 21(6), 941–958. https://doi.org/10.1080/14680777.2020.1732439 3. Albawardi, A., & Jones, R. H. (2021, October 18). Saudi women driving images, stereotyping and digital media. Visual Communication, 22(1), 96–127. https://doi.org/10.1177/14703572211040851 4. Allport, Gordon. W. The nature of prejudice. Addison-Wesley Pub. Co. 1979. 5. Arriagada, A., & Ibáñez, F. (2020, July). “You Need At Least One Picture Daily, if Not, You’re Dead”: Content Creators and Platform Evolution in the Social Media Ecology. Social Media + Society, 6(3), 205630512094462. https://doi.org/10.1177/2056305120944624 6. Bogers, L., Niederer, S., Bardelli, F., & De Gaetano, C. (2020, July 22). Confronting bias in the online representation of pregnancy. Convergence: The International Journal of Research Into New Media Technologies, 26(5–6), 1037–1059. https://doi.org/10.1177/1354856520938606 7. Bol, N., Strycharz, J., Helberger, N., van de Velde, B., & de Vreese, C. H. (2020, October 4). 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 8. Bozdag, E. (2013, June 23). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15(3), 209–227. https://doi.org/10.1007/s10676-013-9321-6 9. Cartwright, P. (2014, December 13). Understanding and Protecting Vulnerable Financial Consumers. Journal of Consumer Policy, 38(2), 119–138. https://doi.org/10.1007/s10603-014-9278-9 10. de Freitas, L. C., & Moura Filho, R. N. D. (2022, November). Aesthetic normalization of gender in the Instagram application: A portrait of the Brazilian woman. Computer Law & Security Review, 47, 105753. https://doi.org/10.1016/j.clsr.2022.105753 11. Elahi, M., Kholgh, D. K., Kiarostami, M. S., Saghari, S., Rad, S. P., & Tkalčič, M. (2021, September). Investigating the impact of recommender systems on user-based and item-based popularity bias. Information Processing & Management, 58(5), 102655. https://doi.org/10.1016/j.ipm.2021.102655 12. Fabris, A., Purpura, A., Silvello, G., & Susto, G. A. (2020, November). Gender stereotype reinforcement: Measuring the gender bias conveyed by ranking algorithms. Information Processing & Management, 57(6), 102377. https://doi.org/10.1016/j.ipm.2020.102377 13. Fosch-Villaronga, E., Poulsen, A., Søraa, R., & Custers, B. (2021, May). A little bird told me your gender: Gender inferences in social media. Information Processing & Management, 58(3), 102541. https://doi.org/10.1016/j.ipm.2021.102541 14. Fosch-Villaronga, E., Poulsen, A., Søraa, R. A., & Custers, B. (2021, May 26). Gendering algorithms in social media. ACM SIGKDD Explorations Newsletter, 23(1), 24–31. https://doi.org/10.1145/3468507.3468512 15. García-Ull, F. J., & Melero-Lázaro, M. (2023, August 24). Gender stereotypes in AI-generated images. El Profesional De La Información. https://doi.org/10.3145/epi.2023.sep.05 16. Glotfelter, A. (2019, December). Algorithmic Circulation: How Content Creators Navigate the Effects of Algorithms on Their Work. Computers and Composition, 54, 102521. https://doi.org/10.1016/j.compcom.2019.102521 17. Hall, P., & Ellis, D. J. (2023, March 14). A systematic review of socio-technical gender bias in AI algorithms. Online Information Review. https://doi.org/10.1108/oir-08-2021-0452 18. Jacobsen, B. N. (2021, October 26). Regimes of recognition on algorithmic media. New Media & Society, 25(12), 3641–3656. https://doi.org/10.1177/14614448211053555 19. Kiesling, S. (2011, December 20). interactional construction of desire as gender. Gender and Language, 5(2), 213–239. https://doi.org/10.1558/genl.v5i2.213 20. Koh, J. (2023, April). “Date me date me”: AI chatbot interactions as a resource for the online construction of masculinity. Discourse, Context & Media, 52, 100681. https://doi.org/10.1016/j.dcm.2023.10068 21. Lambrecht, A., & Tucker, C. (2019, July). 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 22. Matsick, J. L., Kim, L. M., & Kruk, M. (2020, June 10). Facebook LGBTQ Pictivism: The Effects of Women’s Rainbow Profile Filters on Sexual Prejudice and Online Belonging. Psychology of Women Quarterly, 44(3), 342–361. https://doi.org/10.1177/0361684320930566 23. Metaxa, D., Gan, M. A., Goh, S., Hancock, J., & Landay, J. A. (2021, April 13). An Image of Society. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–23. https://doi.org/10.1145/3449100 24. Otterbacher, J., Bates, J., & Clough, P. (2017, May 2). Competent Men and Warm Women. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3025453.3025727 25. Saurabh, S., & Gautam, S. (2019, January). Modelling and statistical analysis of YouTube’s educational videos: A channel Owner’s perspective. Computers & Education, 128, 145–158. https://doi.org/10.1016/j.compedu.2018.09.003 26. Schroeder, J. E. (2020, October 14). Reinscribing gender: social media, algorithms, bias. Journal of Marketing Management, 37(3–4), 376–378. https://doi.org/10.1080/0267257x.2020.1832378 27. Shekhawat, N., Chauhan, A., & Muthiah, S. B. (2019, June 26). Algorithmic Privacy and Gender Bias Issues in Google Ad Settings. Proceedings of the 10th ACM Conference on Web Science. https://doi.org/10.1145/3292522.3326033 28. Siciliano, M. L. (2023, April). Intermediaries in the age of platformized gatekeeping: The case of YouTube “creators” and MCNs in the U.S. Poetics, 97, 101748. https://doi.org/10.1016/j.poetic.2022.101748 29. Singh, V. K., Chayko, M., Inamdar, R., & Floegel, D. (2020, January 22). 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 30. Sokolova, K., Kefi, H., & Dutot, V. (2022, December). Beyond the shallows of physical attractiveness: Perfection and objectifying gaze on Instagram. International Journal of Information Management, 67, 102546. https://doi.org/10.1016/j.ijinfomgt.2022.102546 31. Tang, L., Omar, S. Z., Bolong, J., & Mohd Zawawi, J. W. (2021, April). Social Media Use Among Young People in China: A Systematic Literature Review. SAGE Open, 11(2), 215824402110164. https://doi.org/10.1177/21582440211016421 32. Thelwall, M., & Stuart, E. (2019, July). She’s Reddit: A source of statistically significant gendered interest information? Information Processing & Management, 56(4), 1543–1558. https://doi.org/10.1016/j.ipm.2018.10.007 33. Ulloa, R., Richter, A. C., Makhortykh, M., Urman, A., & Kacperski, C. S. (2022, June 19). Representativeness and face-ism: Gender bias in image search. New Media & Society, 146144482211006. https://doi.org/10.1177/14614448221100699 34. Zhang, M., & Liu, Y. (2021, November). A commentary of TikTok recommendation algorithms in MIT Technology Review 2021. Fundamental Research, 1(6), 846–847. https://doi.org/10.1016/j.fmre.2021.11.015