Analysis on the dissemination mechanism of legal science popularization videos based on meta-semantic coupling network

Analysis on the dissemination mechanism of legal science popularization videos based on meta-semantic coupling network | 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 Analysis on the dissemination mechanism of legal science popularization videos based on meta-semantic coupling network Yaxian Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8066502/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 Short videos on legal science form heterogeneous semantic flows interwoven with titles, tags, and comments on platforms such as Bilibili, and their structural evolution directly determines the reach and sustained popularity of the content. How to uniformly describe semantic diffusion paths in multi-source texts and quantify their impact on the playback-interaction-re-dissemination link has become a core issue in this field; however, existing work primarily focuses on keyword statistics or static graph convolutions, making it challenging to integrate semantics, topology, and diffusion signals into a single, interpretable framework. To this end, this paper proposes the Meta-Semantic Coupling Network (MSCN), which collaboratively depicts the three-layer coupling of "semantics-structure-dissemination" through four modules: cross-source embedding fusion, structural self-attention, temporal consistency constraints, and meta-semantic evolution sequence (MSES). Experiments on 2,232 Chinese legal short videos (2023-12~2024-02) and auxiliary corpora such as CAIL2020-QA show that MSCN reduces the overall MAE by 28% compared with the strongest baseline and compresses the cold start bucket error from 40%-50% to ≈16%; parameter sensitivity positioning α = 0.6, β = 0.5, τ = 5 is the optimal configuration. The results further reveal that the legal semantic network has a three-layer structure, characterized by a "hub-bridge-long tail" configuration. MSCN can amplify the gain of strong content and provide significant buffering for weak content, providing a transferable paradigm for cross-platform multimodal knowledge diffusion. Artificial Intelligence and Machine Learning Legal short video Meta-semantic coupling Dynamic heterogeneous graph Propagation prediction Semantic evolution 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-8066502","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542064134,"identity":"567a65e5-1774-40b9-b072-d3b8d1cbb3f1","order_by":0,"name":"Yaxian Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYHACNiA+IMfG3pD4AMji4SNOS8IBY36eA48NQFrYiNWSOHNG4jMJmKV4gcGNHLMHP3/cYdxwIDmt8muOnQwbA/PDRzfwaklLN+xJeMZscOBY2m3ZbclAh7EZG+fg1ZJ8TIIn4TCbwcGetNuS25iBWnjYpPFrSWyT/JNwmMfgMP+3Yslt9cRoST4mDbRFQrKNIY3x47bDhLVInnmWbiyTdtiAn4chWZpx23EeNmYCfuE7nmP28I3N4fo2+QeJH39uq7bnZ29++BifFoULCQgOMw+YxKMcBOT7DyA4jD8IqB4Fo2AUjIKRCQDDH08OgrJEWAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-3280-230X","institution":"University of Bath","correspondingAuthor":true,"prefix":"","firstName":"Yaxian","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-11-09 01:42:14","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-8066502/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8066502/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95657946,"identity":"97a57873-c1a6-4067-a9a9-9326b3485708","added_by":"auto","created_at":"2025-11-11 16:22:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":640837,"visible":true,"origin":"","legend":"","description":"","filename":"Analysisonthedisseminationmechanismoflegalsciencepopularizationvideosbasedonmetasemanticcouplingnetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8066502/v1_covered_30bf0a08-a248-4812-9ff8-15775ca1306b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAnalysis on the dissemination mechanism of legal science popularization videos based on meta-semantic coupling network\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 Bath","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"Legal short video, Meta-semantic coupling, Dynamic heterogeneous graph, Propagation prediction, Semantic evolution","lastPublishedDoi":"10.21203/rs.3.rs-8066502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8066502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShort videos on legal science form heterogeneous semantic flows interwoven with titles, tags, and comments on platforms such as Bilibili, and their structural evolution directly determines the reach and sustained popularity of the content. How to uniformly describe semantic diffusion paths in multi-source texts and quantify their impact on the playback-interaction-re-dissemination link has become a core issue in this field; however, existing work primarily focuses on keyword statistics or static graph convolutions, making it challenging to integrate semantics, topology, and diffusion signals into a single, interpretable framework. To this end, this paper proposes the Meta-Semantic Coupling Network (MSCN), which collaboratively depicts the three-layer coupling of \"semantics-structure-dissemination\" through four modules: cross-source embedding fusion, structural self-attention, temporal consistency constraints, and meta-semantic evolution sequence (MSES). Experiments on 2,232 Chinese legal short videos (2023-12~2024-02) and auxiliary corpora such as CAIL2020-QA show that MSCN reduces the overall MAE by 28% compared with the strongest baseline and compresses the cold start bucket error from 40%-50% to ≈16%; parameter sensitivity positioning α = 0.6, β = 0.5, τ = 5 is the optimal configuration. The results further reveal that the legal semantic network has a three-layer structure, characterized by a \"hub-bridge-long tail\" configuration. 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