Analyzing the Cultural Landscape of Chinese Art Districts through Online Social Media —A Text Mining and Theoretical Interpretation Using User-Generated Content

Analyzing the Cultural Landscape of Chinese Art Districts through Online Social Media —A Text Mining and Theoretical Interpretation Using User-Generated Content | 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 Article Analyzing the Cultural Landscape of Chinese Art Districts through Online Social Media —A Text Mining and Theoretical Interpretation Using User-Generated Content DONG HAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6611813/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The study examines three Chinese art districts—Beijing 798, Hangzhou Xiangshan, and Jingdezhen Taoxichuan—using 28,661 social media posts. Integrating LDA modeling, irony-aware sentiment analysis, and semantic networks, it uncovers tensions among commercialization, cultural authenticity, and urban identity. Findings reveal 31.2% negative sentiment toward Beijing 798’s over-commercialization, 63.8% positivity for Jingdezhen’s heritage revitalization, and 40% neutrality toward Hangzhou’s emerging spaces. Advancing Lefebvre’s spatial triad, a dynamic equilibrium framework is proposed to balance heritage and development. Methodologically, blending digital humanities tools with cultural theory offers insights into urban renewal and participatory placemaking in the digital era. Humanities/Cultural and media studies Social science/Cultural and media studies Social science/Sociology Art Districts User-Generated Content (UGC) Text Mining LDA (Latent Dirichlet Allocation) Sentiment Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Aug, 2025 Reviews received at journal 11 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor invited by journal 07 Jun, 2025 Editor assigned by journal 07 Jun, 2025 Submission checks completed at journal 29 May, 2025 First submitted to journal 07 May, 2025 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. 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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-6611813","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469719620,"identity":"e869c961-ea4e-433d-9a90-0a3ea5d88a17","order_by":0,"name":"DONG HAO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACNvaGhIMfDCR4+NmbD0CEDhDQwsdz4OFhiQoLOcmeYwnEaZGTSHx8gOdMhbHBjRwD4rSwMSQnHJBsk0hsuJHz8cOPGgY5vhsJjJ8L8Go5lnCgEKilseftZqDjGIwlbyQwS8/Ap4WxB2JLM3vuNgYeNobEDTcS2Jh58Glh5v9wgBeopY0h5xnjn38M9YS1sDEkAL0vYczDkcPGzNvGkGBAUAsPQwIwkCXkJHiOGUvL9kkYzjzzsFkanxb5+Q+SP34wqOOxP9788OObbzbyfMeTD37GpwUdSAAxYwMJGkbBKBgFo2AUYAMACyFPe35AqCEAAAAASUVORK5CYII=","orcid":"","institution":"China Academy of Art","correspondingAuthor":true,"prefix":"","firstName":"DONG","middleName":"","lastName":"HAO","suffix":""}],"badges":[],"createdAt":"2025-05-07 11:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6611813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6611813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84507481,"identity":"ca385831-bbc3-415c-ba40-bc1f741d8298","added_by":"auto","created_at":"2025-06-12 19:10:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":865125,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptWithoutAuthorNames.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6611813/v1_covered_222e2745-527a-4016-8d61-5ddc4ebfd3e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the Cultural Landscape of Chinese Art Districts through Online Social Media —A Text Mining and Theoretical Interpretation Using User-Generated Content","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Art Districts, User-Generated Content (UGC), Text Mining, LDA (Latent Dirichlet Allocation), Sentiment Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6611813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6611813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study examines three Chinese art districts\u0026mdash;Beijing 798, Hangzhou Xiangshan, and Jingdezhen Taoxichuan\u0026mdash;using 28,661 social media posts. 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