Control of Information by a Few: Incoordinated behavior of social bots in information dissemination

Control of Information by a Few: Incoordinated behavior of social bots in information dissemination | 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 Control of Information by a Few: Incoordinated behavior of social bots in information dissemination Quanxin Jia, Wujiong Ren, Ning Luo, Xiaoke Xu, Lun Zhang, Hongzhong Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6279146/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 Social media platforms facilitate the spread of information through posts and shares, with an increasing influence from automated accounts. As AI technologies complicate the monitoring of individual accounts, it becomes crucial to address the unrealistic coordination behaviors exhibited by social bots. This study examines account incoordination in information dissemination by analyzing characteristics, network structures, and dynamic patterns using a co-occurrence network approach. We analyze a dataset of 3,823,020 tweets related to the Bucha event, spanning 959,468 accounts, and extract the interaction network of a critical minority. Accounts are categorized into three types based on their dissemination patterns: government or media accounts, social bots, and human users. Our findings reveal that media or government accounts are the primary sources of information, with both social bots and humans amplifying their messages. Unlike humans, social bots rarely cite other bots as sources, which is a key distinction. Social bots play a significant role in accelerating the spread of media messages and, in some cases, manipulating information flow. These findings highlight the need to monitor and regulate social bot activities, particularly in relation to media and government sources, to maintain the integrity of public discourse. Social science/Complex networks Social science/Cultural and media studies Social bot social network analysis information dissemination 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. 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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-6279146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":499097033,"identity":"dea72e8d-4779-444e-a58b-a17a528471a3","order_by":0,"name":"Quanxin Jia","email":"","orcid":"","institution":"University of Macau","correspondingAuthor":false,"prefix":"","firstName":"Quanxin","middleName":"","lastName":"Jia","suffix":""},{"id":499097034,"identity":"0dd0e48b-cd07-496d-a780-dc0db75d42a4","order_by":1,"name":"Wujiong Ren","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wujiong","middleName":"","lastName":"Ren","suffix":""},{"id":499097035,"identity":"202f58e8-bc4f-4efe-8981-a95734298a78","order_by":2,"name":"Ning Luo","email":"","orcid":"","institution":"Dalian Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Luo","suffix":""},{"id":499097036,"identity":"7c600d14-d622-43e8-b4ed-6ea7fbbde286","order_by":3,"name":"Xiaoke Xu","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoke","middleName":"","lastName":"Xu","suffix":""},{"id":499097037,"identity":"04884c7e-f661-4149-b7c2-1bbd2fa14210","order_by":4,"name":"Lun Zhang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lun","middleName":"","lastName":"Zhang","suffix":""},{"id":499097038,"identity":"77c1dc80-4a22-4d76-9ac6-2607c3fb896e","order_by":5,"name":"Hongzhong Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACAxDB2AAk2HsY4GwitfCcIVmLRA6RWszZew+//LnDJk8+8u2xxzwMNrIbDjA/e4BPi2XPuTQLyTNpxYa389KNeRjSjDccYDM3wOuwGzlmBoZthxM3zs4xk+ZhOJy44QAPmwRBLYkgLTPPgLT8J0qL8YODQC3zJXhAWg4QoeXMGTPGxra0xA08eWmScwySjWceZjPDr+V4j/HHn202ifPbzx6TeFNhJ9t3vPkZXi1AAHGGwQEwCcTMBNSDlHwAkfINhFWOglEwCkbBCAUAiaVLtCigCy8AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Normal University","correspondingAuthor":true,"prefix":"","firstName":"Hongzhong","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-21 16:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6279146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6279146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99788346,"identity":"0328749a-12eb-4200-9790-e50a439e9f3b","added_by":"auto","created_at":"2026-01-08 12:46:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1453913,"visible":true,"origin":"","legend":"","description":"","filename":"AnonymousMainDocument.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6279146/v1_covered_aa81e4e1-ba48-44a3-ad57-8ce838a78beb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Control of Information by a Few: Incoordinated behavior of social bots in information dissemination","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":"Social bot, social network analysis, information dissemination","lastPublishedDoi":"10.21203/rs.3.rs-6279146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6279146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial media platforms facilitate the spread of information through posts and shares, with an increasing influence from automated accounts. As AI technologies complicate the monitoring of individual accounts, it becomes crucial to address the unrealistic coordination behaviors exhibited by social bots. This study examines account incoordination in information dissemination by analyzing characteristics, network structures, and dynamic patterns using a co-occurrence network approach. We analyze a dataset of 3,823,020 tweets related to the Bucha event, spanning 959,468 accounts, and extract the interaction network of a critical minority. Accounts are categorized into three types based on their dissemination patterns: government or media accounts, social bots, and human users. Our findings reveal that media or government accounts are the primary sources of information, with both social bots and humans amplifying their messages. Unlike humans, social bots rarely cite other bots as sources, which is a key distinction. 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