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Better targeting of consumers: Modeling multifactorial gender and biological sex from Instagram posts

Abstract

Along with the rapidly increasing influence and importance of advertisements and publicity in social networking services (SNS), considerable efforts are being made to provide user-customized services through an understanding of SNS content. Studies on online purchasing patterns based on user attributes have also been conducted; however, these studies used either only experimental methods (e.g., surveys or ethnographic accounts) or simple user attributes (e.g., age, biological sex, and location) for computational user modeling. This paper, through interviews with professional marketers, identifies their needs to understand multifactorial SNS user (potential customers) attributes—gender (i.e., masculine, feminine, androgynous) and biological sex (i.e., male and female) characteristics—for marketing purposes. Based on 33,752 Instagram posts, we develop a deep learning-based, classification model merged with three modalities—image (i.e., VGG16 feature and gesture), text (i.e., linguistic, tag, sentence, and category), and activity (i.e., reply and day). Our model achieves a better performance in classifying three gender types in the male, female, and male + female cases than the traditional machine learning models. Our study results reveal the applicability of identifying gender characteristics from posts in the marketing field.

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Notes

  1. https://www.statista.com/statistics/325587/instagram-global-age-group.

  2. https://bit.ly/2aliuXW.

  3. https://keras.io/.

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Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) Grants funded by the Korea government (Ministry of Science and ICT) (NRF-2017M3C4A7083529, 2017R1C1B5017391).

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Jeon, Y., Jeon, S. & Han, K. Better targeting of consumers: Modeling multifactorial gender and biological sex from Instagram posts. User Model User-Adap Inter 30, 833–866 (2020). https://doi.org/10.1007/s11257-020-09260-w

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Keywords

  • Instagram
  • User attribute modeling
  • Gender
  • Biological sex
  • Data-driven analysis
  • Survey
  • Interview