Demographic Influences on Contemporary Art with Unsupervised Style Embeddings

TL;DR

This paper introduces contempArt, a new dataset of contemporary artworks with social and demographic data, and evaluates unsupervised style embeddings, finding no correlation between visual style and social or demographic factors.

Contribution

The paper presents a novel multi-modal dataset and assesses unsupervised style embedding methods in relation to social and demographic attributes.

Findings

No correlation between visual style and social proximity.

No correlation between visual style and gender.

No correlation between visual style and nationality.

Abstract

Computational art analysis has, through its reliance on classification tasks, prioritised historical datasets in which the artworks are already well sorted with the necessary annotations. Art produced today, on the other hand, is numerous and easily accessible, through the internet and social networks that are used by professional and amateur artists alike to display their work. Although this art, yet unsorted in terms of style and genre, is less suited for supervised analysis, the data sources come with novel information that may help frame the visual content in equally novel ways. As a first step in this direction, we present contempArt, a multi-modal dataset of exclusively contemporary artworks. contempArt is a collection of paintings and drawings, a detailed graph network based on social connections on Instagram and additional socio-demographic information; all attached to 442 artists at the beginning of their career. We evaluate three methods suited for generating unsupervised style embeddings of images and correlate them with the remaining data. We find no connections between visual style on the one hand and social proximity, gender, and nationality on the other.