{"id":436200,"date":"2025-11-06T21:41:29","date_gmt":"2025-11-06T21:41:29","guid":{"rendered":"https:\/\/blog.roblox.com\/?p=40032"},"modified":"2025-11-06T21:41:29","modified_gmt":"2025-11-06T21:41:29","slug":"avatars-and-identity-in-the-metaverse-part-2","status":"publish","type":"post","link":"https:\/\/arcader.org\/news\/avatars-and-identity-in-the-metaverse-part-2\/","title":{"rendered":"Avatars and Identity in the Metaverse, Part 2"},"content":{"rendered":"

Read Part 1 of this blog post from our Data Science team here<\/a>.\u00a0<\/span><\/i><\/p>\n

In our previous post, we analyzed avatar aesthetics to better understand the behaviors of Roblox users. In Part 2, we continue our investigation into avatar and identity to better understand how users\u2019 real-life identifiers intersect with their Roblox identities.\u00a0<\/span><\/p>\n

Age<\/b><\/h2>\n

Age is a substantive but easy place to start. Do older or younger users have different aesthetic preferences? Surprisingly, the answer is largely \u201cno!”<\/span><\/p>\n

\"\"While we observe some<\/i> variability in the age distribution of different clusters, in general there\u2019s more overlap than differentiation, with only a few statistically significant deviations.<\/p>\n

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Geographic identity<\/b><\/h2>\n

Roblox is a global platform, and every culture is unique. This is reflected in our data: when plotting cluster frequencies in different countries, we see dramatic aesthetic variations.\u00a0<\/span><\/p>\n

Sometimes, this is due to demographic reasons: in Saudi Arabia, for instance, there are more self-reported women than men on Roblox, which plays out as a boost in popularity to the female-looking avatar clusters.\u00a0<\/span><\/p>\n

On the other hand, some geographic archetypes might be rooted in culture. In Brazil, we see that male-looking avatars with green sports jerseys and a brown, faux-hawk hairstyle (cluster 15 below) are disproportionately popular relative to other countries. Given Brazil\u2019s national colors, the prominence of soccer there, and the hairstyles of their celebrities, this makes sense.\u00a0<\/span><\/p>\n

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Left: Gabriel Barbosa. Attribution: Marcelo Camargo\/ABr, CC BY 3.0 BR <https:\/\/creativecommons.org\/licenses\/by\/3.0\/br\/deed.en>, via Wikimedia Commons Right: Neymar. Attribution: Ag\u00eancia Brasil Fotografias, CC BY 2.0 <https:\/\/creativecommons.org\/licenses\/by\/2.0>, via Wikimedia Commons<\/p>\n<\/div>\n

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Saudi Arabia Frequency of Clusters (cluster 7 is a female-dominated cluster)<\/span><\/p>\n

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Brazil Frequency of Clusters:<\/span><\/p>\n

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Gender<\/b><\/h2>\n

One of the most striking attributes of our clusters are their lopsided gender splits: most clusters are almost entirely male or female, with only a few heterogeneous groups. Roblox users with an identified gender tend to choose avatars that reflect it.<\/span><\/p>\n

We can quantify this. An exceedingly simple convolutional neural network trained on the thumbnails was able to predict user self-identified gender with 91% accuracy. As an interesting aside, when looking at the users which the model assessed incorrectly, we find 65% to be self-identified females, implying that female users may be less likely to pick obviously female-looking avatars.<\/span><\/p>\n

Gender split over 20 clusters:<\/span><\/p>\n

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Aesthetic extremes: Rthro and Blocky<\/b><\/h2>\n

Two specific archetypes that illustrate the aesthetic diversity in Roblox are \u201cRthro\u201d and \u201cBlocky.\u201d Rthro is a class of \u201chumanoid\u201d tall and thin avatars <\/span>released in 2018<\/span><\/a>, while \u201cBlocky\u201d was an extremely popular aesthetic in earlier days at Roblox (see visual below for examples of each). Both are similarly small-sized clusters today, but could not be more visually differentiated.<\/span><\/p>\n

To compare these specific archetypes, we took a lightweight semi-supervised approach: first we generated a larger number (100) of clusters, then manually labeled them as Rthro and Blocky from visual inspection of samples, and finally grouped clusters of the same label. This technique enables the creation of tighter, more cohesive clusters that also conform to our external criteria for the two classes.<\/span><\/p>\n

Some clusters combined to make Blocky:<\/span><\/p>\n

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Some clusters combined to make Rthro:<\/span><\/p>\n

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First off, this exercise confirmed for us that these clusters are relatively small: they together represent only about 7% of our active user base, with less extreme Roblox body types accounting for the other 93%. Secondly, we find that the aesthetic differences between Rthro and Blocky are reflected in the characteristics of their niche users:\u00a0<\/span><\/p>\n