Abstract
Videoart was founded in 1960, but even in contemporary art it’s still a very common form of creating closeness with spectators. The most important characteristic for this study is the social action that video arts often present. How can we determine if a videoart is actually activist? To explain this process is essential to study the categories that define videoartivism, by Sedeño y Concha (2018), they present six steps that can appear in videoartivism. With this theory as the origin point, the study applies the decision tree (Hall; Frank; Witten, 2011), used usually for complex problems to analyze a group of videoarts and study what determines them as activists or not, using the method of applied research in machine learning. This approach is put in application to investigate how similar these videoarts are, and in some way utilize the method of machine learning in a form that helps the world of videoartivism to produce more knowledge about the resistance of the image (Calderón, 2023). The results show what is found in the 30 videoarts selected from the sesc_videobrasil archive, have in common, and in this case, which aspects are more evident when videoart is most considered activist, to study the image video work (Rancière, 2021).
Presenters
Sofia Sartori Dos SantosStudent, Artes Visuais, UNESP - Universidade Estadual Paulista "Júlio de Mesquita Filho", São Paulo, Brazil Regilene Aparecida Sarzi Ribeiro
Professor, Postgraduate Program in Media and Technology, Universidade Estadual Paulista, São Paulo, Brazil
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2024 Special Focus—Images and Imaginaries from Artificial Intelligence
KEYWORDS
Videoartivism, Decision Tree, Image Resistance, Work of the Image
