Document Type

Article

Publication Date

5-2018

Abstract

Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation platform. We use novelty discovery to segment the avatars, then cluster avatars by region to identify visual trends among low-and high-novelty avatars. We find that avatar customization correlates with increased social activity, and we are able to identify distinct visual trends among the U.S.-region and Japan-region profiles. Among these trends, realistic, idealistic, and creative self-representation can be distinguished. We observe that the realistic self-expression mirrors regional demographics, idealistic self-expression reflects shared mass-media tropes, and creative self-expression propagates within the communities.

Comments

This article was originally published in IEEE Transactions on Games 11, no. 4 (2018), 10.1109/TG.2018.2835776.

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