The #NihilistPenguin: A case of emerging Antarctic networked imaginaries

Background Antarctica is one of the most mediated environments on Earth, encountered by most publics less through direct experience than through images, film, literature, scientific communication, and social media. This article examines how Antarctic geographical imaginaries are reconfigured through platform circulation, using posts associated with the viral hashtag #NihilistPenguin as a case study. The phenomenon originates in Werner Herzog’s Encounters at the End of the World (2007), where an Adélie penguin separates from its colony and walks toward the Antarctic interior, leaving its behaviour suspended between scientific explanation and philosophical uncertainty. Methods The study adopts a non-participant digital ethnographic approach focused on YouTube Shorts associated with #NihilistPenguin. Data were analysed using AI-assisted qualitative coding with Gemini Notebook. Manual multimodal interpretation was conducted on a subset of posts, and a validation exercise compared the results of the AI-assisted and manual coding. Results The findings show that the penguin is frequently anthropomorphised and reframed as an intentional, exhausted, ironic, or existential subject. Antarctica is transformed from a concrete ecological environment into an abstract affective landscape associated with emptiness, solitude, and existential uncertainty. The analysis also demonstrates how social media circulation encourages human-centred interpretations that detach the penguin and the continent from their original ecological context. Conclusions Social media platforms actively reorganise Antarctic territorial imaginaries by transforming Antarctica into a symbolic resource through which contemporary subjectivities are expressed. The article contributes to Antarctic social sciences and humanities, digital ethnography, and visual culture by demonstrating how platform-mediated circulation reshapes the production of geographical and symbolic imaginaries of the continent. It also highlights the potential and limitations of AI-assisted qualitative analysis for large-scale digital ethnographic research.