journal article·2023-09-26Open access

Aesthetic Experience and Popularity Ratings for Controversial and Non-Controversial Artworks Using Machine Learning Ranking

Sofia Vlachou, Michail Panagopoulos

Applied Sciences· Volume 13· Issue 19

Abstract

Currently, a substantial portion of images snapped at exhibitions and galleries on social media demonstrates that aesthetic experience is not restricted to the confines of cultural institutions. The primary objective of this paper is to examine whether the content or aspect of an artwork influences the aesthetic experience of the viewer and to measure the artwork’s social media popularity. To compare controversial works of art with those whose design, qualities, or intended message are non-controversial, we first sought out controversial works. A variety of artworks were revealed on Instagram; thus, the objective was to identify a non-controversial artwork published in the same year as each controversial artwork. We adhered to the complete procedure for cleansing, standardizing, and transforming the data to ensure comparability. Popularity was measured using a ranking algorithm and quantitative approaches for the recognition and statistical measurement of emotions. In addition, the exhaustive literature survey on models of aesthetic experience revealed no link between the experience of art and its social media popularity. Considering this, we have proposed, among other things, a new framework for interacting with art that integrates these parameters. According to the findings, controversial artworks elicited stronger emotions than non-controversial artworks. Furthermore, investigations have determined the three most popular works of art in each category. Under the scrutiny of social media, these results may inspire future research on the popularity of museum artworks and the design of aesthetic experiences.
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Research context

OpenAlex topics: Aesthetic Perception and Analysis · Visual Culture and Art Theory · Creativity in Education and Neuroscience

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Audio & Visual Arts, Ionian University, 49100 Corfu, Greece

Funding

the project HAL (Hub of Art Laboratories) *MIS:5047267* · Greece and the European Union—European Regional Development Fund and implemented at the Ionian University, Corfu · Hub of Art Laboratories

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