  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.
