Computer Vision and Internet Meme Genealogy: An Evaluation of Image Feature Matching as a Technique for Pattern Detection

IF 6.3 1区 文学 Q1 COMMUNICATION Communication Methods and Measures Pub Date : 2022-09-22 DOI:10.1080/19312458.2022.2122423
Cédric Courtois, Thomas Frissen
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Abstract

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.
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计算机视觉与网络模因谱系:图像特征匹配作为模式检测技术的评价
网络模因是数字文化的一个基本方面。尽管是个人表达,但它们大大超越了个人层面,成为广泛的社会、文化和政治叙事的窗口和载体。对模因文化的实证研究正在蓬勃发展,但却特别分散。在人文科学和社会科学领域,大多数研究都涉及对大多数精心挑选的模因例子进行深入的语言和视觉分析,以回避对这些特定表达的起源和含义的问题。在计算机科学等技术学科中,努力的重点是模因图像的大规模识别和分类,以及大规模的“病毒”传播模式。本文旨在通过引入一种适用于“基于计算的理论”研究的三步方法来弥合深度和规模之间的鸿沟,其中(1)自动程序建立从大规模语料库中提取的模因图像之间的正式联系,为(2)网络分析推断相关性和传播模式铺平道路,以及(3)实际分类文件夹中的视觉相关图像,以便进一步进行局部解释性分析。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。讨论了未来的前景和应用领域。
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来源期刊
CiteScore
21.10
自引率
1.80%
发文量
9
期刊介绍: Communication Methods and Measures aims to achieve several goals in the field of communication research. Firstly, it aims to bring attention to and showcase developments in both qualitative and quantitative research methodologies to communication scholars. This journal serves as a platform for researchers across the field to discuss and disseminate methodological tools and approaches. Additionally, Communication Methods and Measures seeks to improve research design and analysis practices by offering suggestions for improvement. It aims to introduce new methods of measurement that are valuable to communication scientists or enhance existing methods. The journal encourages submissions that focus on methods for enhancing research design and theory testing, employing both quantitative and qualitative approaches. Furthermore, the journal is open to articles devoted to exploring the epistemological aspects relevant to communication research methodologies. It welcomes well-written manuscripts that demonstrate the use of methods and articles that highlight the advantages of lesser-known or newer methods over those traditionally used in communication. In summary, Communication Methods and Measures strives to advance the field of communication research by showcasing and discussing innovative methodologies, improving research practices, and introducing new measurement methods.
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