计算机科学
分类
数据科学
反问句
技术交流
比例(比率)
阅读(过程)
万维网
订单(交换)
多媒体
人工智能
工程类
电气工程
哲学
语言学
物理
财务
量子力学
法学
政治学
经济
作者
Daniel Liddle,John Timothy Sherrill
标识
DOI:10.1145/3615335.3623005
摘要
The YouTube platform plays a vital role in contemporary technical communication as a central warehouse of technical instructions. Though several studies have explored the importance of specific videos, creators, and communities, it is also necessary to study the platform with a wider, data-driven lens in order to consider rhetorical strategies across channels and communities. But while this relationship between close and distant reading has been applied successfully to text-focused platforms, YouTube's multimodality leads to challenges of scale and categorization for researchers taking a data-driven approach. This paper presents methodological insights from a study sampling and analyzing 300 YouTube videos. The authors reflect on the methodological challenges and propose potential solutions.
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