Cutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments

IF 3 2区社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Social Science Computer Review Pub Date : 2023-05-16 DOI:10.1177/08944393231173895

A. M. Möller, Susan A. M. Vermeer, Susanne E. Baumgartner

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Abstract

Social scientists often study comments on YouTube to learn about people’s attitudes towards and experiences of online videos. However, not all YouTube comments are relevant in the sense that they reflect individuals’ thoughts about, or experiences of the content of a video or its artist/maker. Therefore, the present paper employs Supervised Machine Learning to automatically assess comments written in response to music videos in terms of their relevance. For those comments that are relevant, we also assess why they are relevant. Our results indicate that most YouTube comments are relevant (approx. 78%). Among those, most are relevant because they include a positive evaluation of the video, describe a viewer’s personal experience related to the video, or express a sense of community among the video viewers. We conclude that Supervised Machine Learning is a suitable method to find those YouTube comments that are relevant to scholars studying viewers’ reactions to online videos, and we present suggestions for scholars wanting to apply the same technique in their own projects.

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切断评论混乱:一种有监督的机器学习方法来识别相关的YouTube评论

社会科学家经常研究YouTube上的评论，以了解人们对在线视频的态度和体验。然而，并非所有YouTube评论都是相关的，因为它们反映了个人对视频或其艺术家/制作者内容的想法或体验。因此，本文采用监督机器学习来自动评估针对音乐视频撰写的评论的相关性。对于那些相关的评论，我们也评估它们为什么相关。我们的研究结果表明，大多数YouTube评论都是相关的（约78%）。其中，大多数都是相关的，因为它们包括对视频的积极评价，描述观众与视频相关的个人体验，或表达视频观众之间的社区感。我们得出的结论是，监督机器学习是一种合适的方法，可以找到与研究观众对在线视频反应的学者相关的YouTube评论，我们为希望在自己的项目中应用相同技术的学者提出了建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Social Science Computer Review 社会科学-计算机：跨学科应用

CiteScore

9.00

自引率

4.90%

发文量

审稿时长

>12 weeks

期刊介绍： Unique Scope Social Science Computer Review is an interdisciplinary journal covering social science instructional and research applications of computing, as well as societal impacts of informational technology. Topics included: artificial intelligence, business, computational social science theory, computer-assisted survey research, computer-based qualitative analysis, computer simulation, economic modeling, electronic modeling, electronic publishing, geographic information systems, instrumentation and research tools, public administration, social impacts of computing and telecommunications, software evaluation, world-wide web resources for social scientists. Interdisciplinary Nature Because the Uses and impacts of computing are interdisciplinary, so is Social Science Computer Review. The journal is of direct relevance to scholars and scientists in a wide variety of disciplines. In its pages you''ll find work in the following areas: sociology, anthropology, political science, economics, psychology, computer literacy, computer applications, and methodology.