Snacking Around the World

Shalin Hai-Jew
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Abstract

Social media platforms enable access to large image sets for research, but there are few if any non-theoretical approaches to image analysis, categorization, and coding. Based on two image sets labeled by the #snack hashtag (on Instagram), a systematic and open inductive approach to identifying conceptual image categories was developed, and unique research questions designed. By systematically categorizing imagery in a bottom-up way, researchers may (1) describe and assess the image set contents and categorize them in multiple ways independent of a theoretical framework (and its potential biasing effects); (2) conceptualize what may be knowable from the image set by the defining of research questions that may be addressed in the empirical data; (3) categorize the available imagery broadly and in multiple ways as a precursor step to further exploration (e.g., research design, image coding, and development of a research codebook). This work informs the exploration and analysis of mobile-created contents for open learning.
世界各地的零食
社交媒体平台允许访问大型图像集进行研究,但很少有非理论的方法来进行图像分析,分类和编码。基于#snack标签(在Instagram上)标记的两个图像集,开发了一种系统的、开放的归纳方法来识别概念图像类别,并设计了独特的研究问题。通过以自下而上的方式对图像进行系统分类,研究人员可以(1)描述和评估图像集内容,并以多种方式对其进行分类,而不依赖于理论框架(及其潜在的偏差效应);(2)通过定义可能在实证数据中解决的研究问题,将可能从图像集可知的内容概念化;(3)以多种方式对可用图像进行广泛分类,作为进一步探索的先导步骤(例如,研究设计,图像编码和研究代码本的开发)。这项工作为开放学习的移动创建内容的探索和分析提供了信息。
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