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

Title
Better targeting of consumers: Modeling multifactorial gender and biological sex from Instagram posts
Author
한경식
Keywords
Instagram; User attribute modeling; Gender; Biological sex; Data-driven analysis; Survey; Interview
Issue Date
2020-03
Publisher
SPRINGER
Citation
USER MODELING AND USER-ADAPTED INTERACTION, v. 30, no. 5, page. 833-866
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.
URI
https://link.springer.com/article/10.1007%2Fs11257-020-09260-whttps://repository.hanyang.ac.kr/handle/20.500.11754/165632
ISSN
0924-1868; 1573-1391
DOI
10.1007/s11257-020-09260-w
Appears in Collections:
ETC[S] > 연구정보
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