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Perceived gendered self-representation on Tinder using machine learning

Author

Listed:
  • Yan Asadchy

    (Tallinn University)

  • Andres Karjus

    (Tallinn University)

  • Ksenia Mukhina

    (Tallinn University)

  • Maximilian Schich

    (Tallinn University)

Abstract

This paper explores the gendered differences between men and women as perceived through the images on the online dating platform Tinder. While personal images on Instagram, Tumblr, and Facebook have been studied en masse, large-scale studies of the landscape of visual representations on online dating platforms remain rare. We apply a machine learning algorithm to 10,680 profile images collected on Tinder in Estonia to study the perceived gendered differences in self-representation among men and women. Beyond identifying the dominant genres of profile pictures used by men and women, we build a comprehensive map of visual self-representation on the platform. We further expand our findings by analyzing the distribution of the image genres across the profile gallery and identifying the prevalent positions for each genre within the profiles. Lastly, we identify the variability of women’s and men’s images within each genre. Our approach provides a holistic overview of the culture of visual self-representation on the dating app Tinder and invites scholars to expand the research on gendered differences and stereotypes to include cross-platform and cross-cultural analysis.

Suggested Citation

  • Yan Asadchy & Andres Karjus & Ksenia Mukhina & Maximilian Schich, 2024. "Perceived gendered self-representation on Tinder using machine learning," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03801-z
    DOI: 10.1057/s41599-024-03801-z
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    References listed on IDEAS

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    1. Athey, Susan & Karlan, Dean & Palikot, Emil & Yuan, Yuan, 2022. "Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces," Research Papers 4071, Stanford University, Graduate School of Business.
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    Cited by:

    1. Andres Karjus, 2025. "Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-18, December.

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