Modelling TikTok Video Category Transitions Using Markov Chains

Modelling TikTok Video Category Transitions Using Markov Chains | 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 Method Article Modelling TikTok Video Category Transitions Using Markov Chains Abdulrahman Jamaleddine, Olamide Agunbiade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7637701/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 In this paper, we utilise discrete-time Markov chains to investigate the transition between TikTok video categories. We collected a sample of 65 videos in six categories: motivational, dance, comedy, lifestyle, educational, and food. From the sequence, we constructed a transition matrix and examined the basic properties of the chain. We then found the stationary distribution, which shows the long-term pattern of categories if the same viewing behaviour continues. Applied Mathematics Markov Chains Stochastic Processes Mathematical Modelling Social Media Probability Theory 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-7637701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":516378361,"identity":"26bee113-a515-4fe3-944e-408d87118096","order_by":0,"name":"Abdulrahman Jamaleddine","email":"data:image/png;base64,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","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Abdulrahman","middleName":"","lastName":"Jamaleddine","suffix":""},{"id":516378709,"identity":"1008baec-a1af-4bfa-bf87-99f17ef3cb4c","order_by":1,"name":"Olamide Agunbiade","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Olamide","middleName":"","lastName":"Agunbiade","suffix":""}],"badges":[],"createdAt":"2025-09-17 08:32:44","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-7637701/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7637701/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91580061,"identity":"b4210056-0d89-4800-8f79-c7a97fece814","added_by":"auto","created_at":"2025-09-18 03:49:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":330524,"visible":true,"origin":"","legend":"","description":"","filename":"ModellingTikTokVideoCategoryTransitionsUsingMarkovChains.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7637701/v1_covered_46a98f1d-bdce-4a82-b2f0-80ae649c9a37.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eModelling TikTok Video Category Transitions Using Markov Chains\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Ibadan","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":"Markov Chains, Stochastic Processes, Mathematical Modelling, Social Media, Probability Theory","lastPublishedDoi":"10.21203/rs.3.rs-7637701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7637701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we utilise discrete-time Markov chains to investigate the transition between TikTok video categories. We collected a sample of 65 videos in six categories: motivational, dance, comedy, lifestyle, educational, and food. From the sequence, we constructed a transition matrix and examined the basic properties of the chain. We then found the stationary distribution, which shows the long-term pattern of categories if the same viewing behaviour continues.\u003c/p\u003e","manuscriptTitle":"Modelling TikTok Video Category Transitions Using Markov Chains","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 03:41:27","doi":"10.21203/rs.3.rs-7637701/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d2ee381c-dce6-4844-8e37-c144784ce49c","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54862276,"name":"Applied Mathematics"}],"tags":[],"updatedAt":"2025-09-18T03:41:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 03:41:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7637701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7637701","identity":"rs-7637701","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}