Critical Climate Machine: A Visual and Musical Exploration of Climate Misinformation through Machine Learning

Abstract

Critical Climate Machine is a cutting-edge media art installation that critically exposes and quantifies mechanisms of climate change misinformation. Utilizing computational aesthetics across data, imagery, and sound, this work processes real-time data from X (Twitter) through a natural language processing learning model derived from cognitive sciences. It not only renders the statistical aspects of this data visually but also manifests its thermal effects. A unique audio dimension is introduced through dialogues between climate skeptics and climate advocates, processed by the generative machine learning (ML) algorithm Dicy2. These elements collectively shape the installation, each unveiling its distinctive algorithmic aesthetics and technical underpinnings. This paper concentrates on the dual application of ML algorithms: one for dissecting extensive online misinformation streams, and the other for creating climate-related dialogues. This dual approach opens a discussion on the mediation of climate, at the convergence of computational and physical realms. Our aim is to critically examine the role of ML technologies in crafting aesthetic experiences that resonate within scientific discourse and public debate on climate issues.