Improving Social Media Popularity Prediction with Content-aware Post Dependencies

Improving Social Media Popularity Prediction with Content-aware Post Dependencies | 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 Improving Social Media Popularity Prediction with Content-aware Post Dependencies Zhizhen Zhang, Xiaohui Xie, Mengyu Yang, Ye Tian, Lanshan Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4267015/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 Numerous social media posts exhibit inherent qualities that determine their popularity even before being published. Consequently, comprehending the patterns of post popularity and forecasting them before publication holds significant practical value. Existing work mainly utilizes information from individual posts to make predictions, ignoring the popularity correlation between different posts. By analyzing the dataset, we find that there is an important relationship between the content and popularity of different posts published in a short period. To address this challenge, we introduce Content-aware Post Dependency Networks (CPDN) that utilize information from content-related posts to improve the popularity prediction of target posts. CPDN employs multimodal feature adapters to create task-specific representations for target posts. Utilizing hierarchical category information, it captures nuanced correlations between posts. The model incorporates recurrent networks with gating layers and attention mechanisms to aggregate posts with correlated content for better prediction of target posts. The experimental results on Social Media Popularity Dataset under two different settings demonstrate the superiority of our method over the state-of-the-art methods. We release our code in \href{ https://github.com/Daisy-zzz/CPDN.git}{Daisy-zzz/CPDN} for reproducibility. Popularity Prediction Social Media Analysis Full Text Additional Declarations No competing interests reported. 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. 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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-4267015","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291655318,"identity":"4f67f14b-b7be-4291-b0d3-a4681c644eff","order_by":0,"name":"Zhizhen Zhang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Zhizhen","middleName":"","lastName":"Zhang","suffix":""},{"id":291655322,"identity":"b65326f5-c7cc-45d6-9dfd-8b9e1e1d05c3","order_by":1,"name":"Xiaohui Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIie3RMUvDQBTA8RcObrqSNV38DC84WtqvckcgWUQKQieHSKGTdE7pl7CTuEUeNIu0a4YO16WTQ1w0g4qX4OZRMxa8P+/ghvtxHAfgcp1izCzZDGM5ALZj4p0IV2k38pNRIkyb3Z8EC7YO9M3gCgvxvq/Hu+FDEGmoJgT+MrWS/pRHKNfxNVJvNRV4iB6zGL1sQxDscivxmQi15KTuGwJIEZYSWW9GgIG0Es78KpdfDRH727olScU+jxBzi6fVrCVeKpCGWF4i844Q85ZzVPNYLYiHS0MkPr+Mn+42iQhKO8EtHfr120DNt6Rf6w8aYZGsdD25OPMzO/mV+U+A3CzR7bxp1Pmky+Vy/Zu+AYGiZAfaGevXAAAAAElFTkSuQmCC","orcid":"","institution":"Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Xie","suffix":""},{"id":291655325,"identity":"62d9f81e-6ebe-4f28-be3e-d0c6bc3158dd","order_by":2,"name":"Mengyu Yang","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Yang","suffix":""},{"id":291655328,"identity":"1c0abaf3-c5e6-47e6-8f00-5c7d99e44e39","order_by":3,"name":"Ye Tian","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Tian","suffix":""},{"id":291655331,"identity":"a811ca47-f7f1-4de6-a48d-bbeea39d0f1f","order_by":4,"name":"Lanshan Zhang","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Lanshan","middleName":"","lastName":"Zhang","suffix":""},{"id":291655333,"identity":"3126fa56-bd26-4c82-a9cd-92f6bd997ff9","order_by":5,"name":"Yong Jiang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-04-15 03:50:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4267015/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4267015/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55264138,"identity":"3fe6b5dc-0dc6-4a3b-8caa-d19fc48f541c","added_by":"auto","created_at":"2024-04-25 01:37:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":610392,"visible":true,"origin":"","legend":"","description":"","filename":"CPDNWWWJ.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4267015/v1_covered_b6508e33-1a92-4008-b4b3-6eb4926ff76d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Social Media Popularity Prediction with Content-aware Post Dependencies","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Popularity Prediction, Social Media Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4267015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4267015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Numerous social media posts exhibit inherent qualities that determine their popularity even before being published. Consequently, comprehending the patterns of post popularity and forecasting them before publication holds significant practical value. Existing work mainly utilizes information from individual posts to make predictions, ignoring the popularity correlation between different posts. By analyzing the dataset, we find that there is an important relationship between the content and popularity of different posts published in a short period. To address this challenge, we introduce Content-aware Post Dependency Networks (CPDN) that utilize information from content-related posts to improve the popularity prediction of target posts. CPDN employs multimodal feature adapters to create task-specific representations for target posts. Utilizing hierarchical category information, it captures nuanced correlations between posts. The model incorporates recurrent networks with gating layers and attention mechanisms to aggregate posts with correlated content for better prediction of target posts. 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