ABSTRACT

Internet memes are a fundamental aspect of digital culture. Despite being individual expressions, they vastly transcend the individual level as windows into and vehicles for wide-stretching social, cultural, and political narratives. Empirical research into meme culture is thriving, yet particularly compartmentalized. In the humanities and social sciences, most efforts involve in-depth linguistic and visual analyses of mostly handpicked examples of memes, begging the question on the origins and meanings of those particular expressions. In technical disciplines, such as computer science, efforts are focused on the large-scale identification and classification of meme images, as well as patterns of “viral” spread at scale. This contribution aims to bridge the chasm between depth and scale by introducing a three-step approach suitable for “computational grounded theoretical” studies in which (1) an automated procedure establishes formal links between meme images drawn from a large-scale corpus paving the way for (2) network analysis to infer patterns of relatedness and spread, and (3) practically classifying visually related images in file folders for the purpose of further local, hermeneutic analysis. The procedure is demonstrated and evaluated on two datasets: an artificially constructed, structured dataset and a naturally harvested unstructured dataset. Future horizons and domains of application are discussed.

摘要

互联网模因是数字文化的一个基本方面。尽管是个人表达，但它们极大地超越了个人层面，成为广泛社会、文化和政治叙事的窗口和工具。对模因文化的实证研究正在蓬勃发展，但特别分散。在人文和社会科学领域，大多数努力都涉及对大多数精心挑选的模因示例进行深入的语言和视觉分析，从而回避关于这些特定表达方式的起源和含义的问题。在计算机科学等技术学科中，工作重点是模因图像的大规模识别和分类，以及“病毒”大规模传播的模式。这一贡献旨在通过引入适用于“计算基础理论”研究的三步法来弥合深度和尺度之间的鸿沟，其中 (1) 自动化程序在从大规模语料库中提取的模因图像之间建立正式联系，铺平了道路(2) 网络分析以推断相关性和传播模式，以及 (3) 对文件夹中的视觉相关图像进行实际分类，以进行进一步的本地解释学分析。该过程在两个数据集上进行了演示和评估：人工构建的结构化数据集和自然收获的非结构化数据集。讨论了未来的视野和应用领域。