Is 'art' simply 'content' in the age of social media? What does art's creation, appreciation, and experience lose when art becomes content? This exploratory research employs a mixed methods approach encompassing interviews, co-design, and stakeholder reviews to critically examine the perceptions of artists, curators, and content creators regarding the distinction between 'art' and 'content.' Our participants reveal four characteristics essential for art experiences that we use to co-design new digital 'content' environments. The work concludes with content creators reviewing the characteristics and designs, offering their thoughts on the feasibility and potential future for 'art' on social media. We offer open-source access to all design and research materials. By exploring the divide between 'art' and 'content,' this research contributes insights and guidelines for developing alternative algorithmic experiences to support creativity on social media.

Contemporary art consumption is predominantly online, driven by algorithmic recommendation systems that dictate artwork visibility. Despite not being designed for curation, these algorithms’ machinic ways of seeing play a pivotal role in shaping visual culture, influencing artistic creation, visibility, and associated social and financial benefits. The Algorithmic Pedestal was a gallery, practice-based research project that reported gallerygoers’ perceptions of a human’s curation and curation achieved by Instagram’s algorithm. This paper presents a technical analysis of the same exhibit using computer vision code, offering insights into machines’ perception of visual art. The computer vision code assigned values on various metrics to each image, allowing statistical comparisons to identify differences between the collections of images selected by the human and the algorithmic system. The analysis reveals statistically significant differences between the exhibited images and the broader Metropolitan Museum of Art digital collection. However, the analysis found minimal distinctions between human-curated and Instagram-curated images. This study contributes insights into the perceived value of the curation process, shedding light on how audiences perceive artworks differently from machines using computer vision.

Many mobile apps are designed not just to support end-users’ needs, but also commercial aims. This can result in app designs that compromise end-user privacy, safety, and well-being. Since apps nowadays provide vital digital information and services, users often have no choice but to accept potentially harmful or manipulative app designs. What if, instead, individuals could customise their apps to make them safer and better suit their needs? This exploratory work examines this question through a multi-faceted approach; first, to understand user needs, we conducted a survey (n = 100) of changes users wanted in their apps, and of perceptions of risks in app repair. Second, to identify technical challenges, we developed a prototype that enables end-users to change their apps, and realised several modifications suggested by survey participants. Finally, we conduct a set of expert interviews (n = 8) to delve into the ethical and legal aspects of such a tool, and synthesise a framework of risks and opportunities of app repair.

Throughout history, a complex network of human actors shaped how the general public perceived art. Today, social media platforms and their algorithms influence artful experiences for billions. How has this changed the appreciation and perception of art? The role of visual art as items we use to define ourselves and our societies motivates research to explore how recommendation algorithms impact our ability to appreciate and perceive art. There are three aspects to explore: the art metadata and algorithm functionality, conversations with artworld experts, and redesigning digital art experiences. These three methods will follow open science practices and methods by releasing open-access datasets and research prototypes. Ultimately, this proposed thesis aims to contribute to theories of content base algorithmic recommendation and its role in presenting art and culture to users.