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M. ; Vermeer, S.A. M. ; Baumgartner, S. E.  \nDOI  \n10.1177/08944393231173895  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nPublished in  \nSocial Science Computer Review  \nLicense  \nCC BY-NC  \nLink to publication  \nCitation for published version (APA):  \nMöller, 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.  \n[https://doi.org/10.1177/08944393231173895](https://doi.org/10.1177/08944393231173895)  \nGeneral rights  \nIt 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) .  \nDisclaimer/Complaints regulations  \nIf 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, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-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))  \nDownload date:18 Oct 2024  \nArticle  \nCutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments  \nSocial Science Computer Review 2024, Vol. 42(1) 162–185 © The Author(s) 2023  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)  \n[DOI: 10.1177/08944393231173895](DOI: 10.1177/08944393231173895)[ ](DOI: 10.1177/08944393231173895)[journals.sagepub.com/home/ssc](journals.sagepub.com/home/ssc)  \nA. Marthe Mller 1 􀀁, Susan A. M. Vermeer 1 􀀁 , and Susanne E. Baumgartner 1 􀀁  \nAbstract  \nSocial 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.  \nKeywords  \nuser comments, YouTube, relevance, Supervised Machine Learning, music videos  \nIntroduction  \nPeople ’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,  \n1 Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amster","cbCait1Oj4mlIp0Q","https://ap.wps.com/l/cbCait1Oj4mlIp0Q","pdf",802670,25,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"How does the paper define “relevance” for YouTube comments?\",\"answer\":\"A comment is relevant when it reflects the viewer’s experiences, opinions, or thoughts about the video content or its artist/maker. It also counts as relevant when it relates to the social experience of watching on a platform, such as referring to other users or asking for information.\"},{\"question\":\"What method is used to assess YouTube comment relevance?\",\"answer\":\"The study employs supervised machine learning to automatically assess comments written in response to music videos for relevance.\"},{\"question\":\"What reasons explain why most relevant comments are relevant?\",\"answer\":\"Most relevant comments are relevant because they include positive evaluations, describe the viewer’s personal experience related to the video, or express a sense of community among video viewers.\"}]","Cutting Through the Comment Chaos - A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments | PDF",63]