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Finding the future in digitally mediated ruin: #nostalgiacores and the algorithmic culture of digital platforms

计算机科学 数字媒体 万维网 数字文化 数字图像 人机交互 多媒体 数据科学 人工智能 图像(数学) 图像处理 社会学 媒体研究
作者
Maria Gemma Brown,Nicholas Carah,Xue Ying Tan,Daniel Angus,Jean Burgess
出处
期刊:Convergence [SAGE Publishing]
标识
DOI:10.1177/13548565241270669
摘要

The #nostalgiacores are a series of interrelated hashtags on Instagram and TikTok where users recirculate content from the digital and consumer cultures of the 1990s and 2000s – childhood play centres, dead malls, long-gone toys, and superseded game consoles and phones. In this article, we explore these digital cultures using a critical platform studies approach that involves a combination of network analysis and close textual analysis augmented with purpose-built machine vision tools. We scrape a collection of 359,150 images from Instagram that used one or more of 30 ‘-cores’ hashtags (such as #y2kcore, #webcore and #childhoodcore) that we chose following a period of immersive qualitative investigation of #nostalgiacore scenes on Instagram during 2021 and 2022. 10,000 Instagram images were then randomly selected and processed using a purpose-built unsupervised machine vision model that clusters images together based on their similarities. This research is part of a multi-year project where we develop hybrid digital methods for critically simulating and exploring the interplay between our image-making practices and the algorithmic systems that cluster and curate them. By combining computational approaches with critical platform and cultural studies approaches we speculatively explore both practices of curation and their interplay with the algorithmic classification and recommendation models of digital platforms. Our platform-oriented mode of textual analysis helps us to explore how our digital cultures are both symbolically and technically nostalgic. Instagram users in the #nostalgiacore scene recirculate images from the past as part of practices of critically reflecting on digital platforms and consumer cultures. At the same time those images are recuperated as archives used to train the algorithmic models that optimise attention on digital media platforms like Instagram.
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