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

Aug 4, 2026

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Social scientists often analyze comments on YouTube to understand users’ attitudes and experiences with online videos. Not all comments, however, meaningfully reflect viewers’ thoughts about—or personal experiences with—the video content or its artist/maker. This paper applies supervised machine learning to automatically evaluate the relevance of comments responding to music videos and explains why relevant comments are relevant. Results show most comments are relevant (about 78%), typically due to positive evaluations, personal experience descriptions, or expressions of community among viewers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

Abstract

Introduction

Relevance of YouTube comments

Challenges from irrelevant and spam comments

Research approach (implied)

UvA-DARE (Digital Academic Repository) Cutting Through the Comment Chaos A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments Möller, A. M. ; Vermeer, S.A. M. ; Baumgartner, S. E. DOI 10.1177/08944393231173895 Publication date 2024 Document Version Final published version Published in Social Science Computer Review License CC BY-NC Link to publication Citation for published version (APA): Möller, A. M. , Vermeer, S. A. M. , & Baumgartner, S. E. (2024) . Cutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments. Social Science Computer Review, 42(1), 162-185. [https://doi.org/10.1177/08944393231173895](https://doi.org/10.1177/08944393231173895) General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) . Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, P.O. Box 19185 , 1000 GD Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl)) Download date:03 Aug 2026 Article Cutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments Social Science Computer Review 2024, Vol. 42(1) 162–185 © The Author(s) 2023 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions) [DOI: 10.1177/08944393231173895](DOI: 10.1177/08944393231173895)[ ](DOI: 10.1177/08944393231173895)[journals.sagepub.com/home/ssc](journals.sagepub.com/home/ssc) A. Marthe Mller 1 􀀁, Susan A. M. Vermeer 1 􀀁 , and Susanne E. Baumgartner 1 􀀁 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. Keywords user comments, YouTube, relevance, Supervised Machine Learning, music videos Introduction People ’s daily use of social media has made interpersonal communication via such platforms a frequently studied topic among social scientists. A speciﬁc focus within this research is the study of online user comments written on social media and on YouTube in particular. It has been shown, 1 Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV A

FAQs

What does “relevant” mean for YouTube comments in this paper?

A comment is considered relevant when it reflects an individual’s experiences, opinions, or thoughts about the video content or its artist/maker, including comments tied to the social experience of watching on the platform.

How does the study determine comment relevance?

The paper uses supervised machine learning to automatically assess comments written in response to music videos and to evaluate why the relevant ones are relevant.

What proportion of YouTube comments are found to be relevant?

The results indicate that most YouTube comments are relevant, at approximately 78%, and they are largely relevant due to positive evaluations, personal experiences, or community-related expressions.

Aug 4, 2026

Upload by Liam