Decoding Audience Emotion: Deep Learning and Explainable AI Analysis of Exposure and Valence Effects in China-Related Videos on Youtube

Decoding Audience Emotion: Deep Learning and Explainable AI Analysis of Exposure and Valence Effects in China-Related Videos on Youtube | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decoding Audience Emotion: Deep Learning and Explainable AI Analysis of Exposure and Valence Effects in China-Related Videos on Youtube Fanghao Zheng, Jie Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8507672/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital media platforms have become central to how people perceive nations, cultures, and global issues. Yet existing research rarely captures how content visibility and emotional tone jointly shape audience responses in UGC platform. Traditional survey or content-analytic methods identify correlations but overlook the nonlinear interactions now traceable through large-scale platform data. Using China-related videos on YouTube as a case study, this research propose a computational framework that links media content, exposure dynamics, and audience sentiments within a unified modeling pipeline. It integrates RoBERTa- and BART-based sentiment quantification with exposure indicators and predict average comment sentiment (ACS) using a multilayer deep neural network (DNN) enhanced with explainable AI (XAI). Results show that Exposure metrics, including view count (23.78%), comment count (20.46%), and subscriber count (18.91%), emerge as the dominant determinants of ACS. SHAP dependence plots indicate an overall positive association between Content Valence and ACS. Exposure metrics display different patterns: views are positively associated with ACS, whereas comment counts are negatively associated. At low exposure levels, ACS and content valence are aligned in polarity; at high exposure levels, the effect weakens or reverses, and negative-valence videos exhibit a U-shaped pattern. An additional finding is that, during medium-to-high exposure periods, videos from high-subscriber accounts maintain persistently negative ACS, whereas those from mid-subscriber accounts show polarity reversals. This research offers a new methodological perspective for computational communication research, extends the application of Second-Level Agenda-Setting to UGC contexts, and provides practical insights for communication strategy. Media Studies Artificial Intelligence and Machine Learning YouTube Deep Learning Explainable AI (XAI) China-related video Second-Level Agenda Setting Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8507672","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568802584,"identity":"e173fbfb-74f5-4b0c-9694-596dcec9ae48","order_by":0,"name":"Fanghao Zheng","email":"","orcid":"https://orcid.org/0009-0005-2943-5854","institution":"University of Nottingham Ningbo China","correspondingAuthor":false,"prefix":"","firstName":"Fanghao","middleName":"","lastName":"Zheng","suffix":""},{"id":568802585,"identity":"17268de9-663d-4469-a123-8188584ac7ba","order_by":1,"name":"Jie Shen","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-6608-8517","institution":"University of Nottingham Ningbo China","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2026-01-03 14:56:51","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8507672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8507672/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99670484,"identity":"f5cb2908-0619-48e1-96f5-2d3abab967bf","added_by":"auto","created_at":"2026-01-07 06:55:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1581778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript11122.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8507672/v1_covered_7340d247-955f-48ca-aa12-df0ca59e3991.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDecoding Audience Emotion: Deep Learning and Explainable AI Analysis of Exposure and Valence Effects in China-Related Videos on Youtube\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Nottingham Ningbo China","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"YouTube, Deep Learning, Explainable AI (XAI), China-related video, Second-Level Agenda Setting","lastPublishedDoi":"10.21203/rs.3.rs-8507672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8507672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital media platforms have become central to how people perceive nations, cultures, and global issues. 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