Saelyne Yang
School of Computing, KAIST, Daejeon, Korea, Republic of
How-to videos are rich in information---they not only give instructions but also provide justifications or descriptions. People seek different information to meet their needs, and identifying different types of information present in the video can improve access to the desired knowledge. Thus, we present a taxonomy of information types in how-to videos. Through an iterative open coding of 4k sentences in 48 videos, 21 information types under 8 categories emerged. The taxonomy represents diverse information types that instructors provide beyond instructions. We first show how our taxonomy can serve as an analytical framework for video navigation systems. Then, we demonstrate through a user study (n=9) how type-based navigation helps participants locate the information they needed. Finally, we discuss how the taxonomy enables a wide range of video-related tasks, such as video authoring, viewing, and analysis. To allow researchers to build upon our taxonomy, we release a dataset of 120 videos containing 9.9k sentences labeled using the taxonomy.
https://doi.org/10.1145/3544548.3581126
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)