MSTBC: X Bot Detection with Multiple Social-Temporal Behavior Contrast

MSTBC: X Bot Detection with Multiple Social-Temporal Behavior Contrast | 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 MSTBC: X Bot Detection with Multiple Social-Temporal Behavior Contrast Zhishu Jiang, Wei Chen, Weijie Zhang, Youfang Lin, Huaiyu Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5699605/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 X bot detection aims to automatically identify malicious X bots on the X platform, playing a crucial role in protecting information and maintaining platform stability.Recently, mixture-based methods primarily simultaneously consider investigating various social features (e.g. user metadata, tweets, and social relationships) of users to differentiate humans and bots, which hold excellent performance. However, two major challenges have not been adequately addressed in current mixture-based methods: (1) Humans and bots exhibit different temporal behavior patterns, which has not been fully explored.(2) Existing mixture-based methods promote the detection by fusing diverse features but overlook the noise accumulation that arises during the fusion process.In this paper, we propose a novel X bot detection method with Multiple Social-Temporal Behavior Contrast (MSTBC), which integrates users' multiple social-temporal behaviors, including the static behavior (description content), social behavior (social structure) and temporal behavior (temporal behavior patterns).Specifically, the fine-grained temporal behaviors of users are represented as four different prompts. A temporal behavior PLM with temporal behavior prompts in MSTBC serves as the encoder to understand temporal behavior patterns.In addition, we employ multi-behavior contrast to minimize the differences of various features of users, alleviating the noise accumulation that arises during the fusion of diverse features.Experimental results demonstrate that MSTBC outperforms state-of-the-art models on four datasets. The code is available at https://anonymous.4open.science/r/MSTBC-C659. X bot detection Graph Attention Network Temporal Behavior Pre-trained Language Model Contrastive Learning 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. 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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-5699605","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":395909848,"identity":"8bf3f773-f26f-4835-b795-a42334886cdd","order_by":0,"name":"Zhishu Jiang","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zhishu","middleName":"","lastName":"Jiang","suffix":""},{"id":395909849,"identity":"a8c5c7a3-ae51-4fc1-8a0f-c25271c3d4da","order_by":1,"name":"Wei Chen","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""},{"id":395909850,"identity":"8de25e50-e3e8-44fd-9f52-9df9f6eed07e","order_by":2,"name":"Weijie Zhang","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Zhang","suffix":""},{"id":395909851,"identity":"1933eff6-a506-4ed6-b9a2-0dd93621e3da","order_by":3,"name":"Youfang Lin","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Youfang","middleName":"","lastName":"Lin","suffix":""},{"id":395909852,"identity":"56461c61-312c-4656-84e8-41533fa0fdbe","order_by":4,"name":"Huaiyu Wan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACAzBZcYAZTPMQr+XMAWYe0rQwth1gIF6LuUSO4efCeXfY7SUSGB+8bWOQNyekxXJGjrH0zG3PmHkkEpgN57YxGO5sIOSwG7kbpHm3HQZpYZPmbWNIMDhAWMvm37xzwFrYfxOrZZs0bwPEFmbitJx5/82a5xjQL2ceNkvOOSdhuIGgluNpybd5au4ks7cnH/zwpsxGnqAtDAIJYCoZGDsNQFqCkHog4IcYakeE0lEwCkbBKBipAAC0Ij3ml+CoPAAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Huaiyu","middleName":"","lastName":"Wan","suffix":""}],"badges":[],"createdAt":"2024-12-23 12:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5699605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5699605/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76983601,"identity":"854e4a73-5e52-41d0-a9d4-a7c06cecc8fd","added_by":"auto","created_at":"2025-02-24 02:31:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1210771,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5699605/v1_covered_95052225-5734-497c-9617-ec515e0d4b2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MSTBC: X Bot Detection with Multiple Social-Temporal Behavior Contrast","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":"X bot detection, Graph Attention Network, Temporal Behavior, Pre-trained Language Model, Contrastive Learning","lastPublishedDoi":"10.21203/rs.3.rs-5699605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5699605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eX bot detection aims to automatically identify malicious X bots on the X platform, playing a crucial role in protecting information and maintaining platform stability.Recently, mixture-based methods primarily simultaneously consider investigating various social features (e.g. user metadata, tweets, and social relationships) of users to differentiate humans and bots, which hold excellent performance. 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