{"paper_id":"33aeaf21-356c-44a5-8de8-2ef0c7d535b2","body_text":"Verified authors shape X/Twitter discursive communities | 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 Verified authors shape X/Twitter discursive communities Stefano Guarino, Ayoub Mounim, Guido Caldarelli, Fabio Saracco This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4388361/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 Community detection algorithms try to extract a mesoscale structure from the available network data, generally avoiding any explicit assumption regarding the quantity and quality of information conveyed by specific sets of edges. In this paper, we show that the core of ideological/discursive communities on X/Twitter can be effectively identified by uncovering the most informative interactions in an authors-audience bipartite network through a maximum-entropy null model. The analysis is performed considering three X/Twitter datasets related to the main political events of 2022 in Italy, using as benchmarks four state-of-the-art algorithms – three descriptive, one inferential –, and manually annotating nearly 300 verified users based on their political affiliation. In terms of information content, the communities obtained with the entropy-based algorithm are comparable to those obtained with some of the benchmarks. However, such a methodology on the authors-audience bipartite network: uses just a small sample of the available data to identify the central users of each community; returns a neater partition of the user set in just a few, easy to interpret, communities; clusters well-known political figures in a way that better matches the political alliances when compared with the benchmarks. Our results provide an important insight into online debates, highlighting that online interaction networks are mostly shaped by the activity of a small set of users who enjoy public visibility even outside social media. 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-4388361\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":304013463,\"identity\":\"f878f300-97eb-4358-b785-9e3b54c48939\",\"order_by\":0,\"name\":\"Stefano Guarino\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYPACCQYG9sYGKIexAY9KZC08B+Fa4JoJ6UqAM/FbI9/Afk3i5x6LPH7Jx40ffjDY5Zk3MLc/wKfF4ABPmWTPM4liydmJzZI9DMnFMgcIOMyAgSdNgueAROKG24kN0gwMBxJnEPKLfANPmuQfkJabB5t/E6WF4QD7MWmwLTcY24izxeAwD7O1DFDLzJ7ENsseg+TEGcyMjTPwOqy9/eHNNwfqEvvZjz++8aPCLnEGe/uDD3gdxsxjgGwpSASvehBgf0BQySgYBaNgFIxwAABUbEeU533YeAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"National Research Council of Italy (CNR)\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Stefano\",\"middleName\":\"\",\"lastName\":\"Guarino\",\"suffix\":\"\"},{\"id\":304013464,\"identity\":\"a769e6c8-4695-4398-a8bd-285b4da3ac9e\",\"order_by\":1,\"name\":\"Ayoub Mounim\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Libera Università Internazionale degli Studi Sociali “Luiss Guido Carli”\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ayoub\",\"middleName\":\"\",\"lastName\":\"Mounim\",\"suffix\":\"\"},{\"id\":304013466,\"identity\":\"7113b8bf-4b08-4013-965c-4cd56c8cedd0\",\"order_by\":2,\"name\":\"Guido Caldarelli\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ca’Foscari University of Venice\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Guido\",\"middleName\":\"\",\"lastName\":\"Caldarelli\",\"suffix\":\"\"},{\"id\":304013467,\"identity\":\"5538402b-4106-4d47-bd77-b60a2d76d236\",\"order_by\":3,\"name\":\"Fabio Saracco\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Enrico Fermi Center for Study and Research\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fabio\",\"middleName\":\"\",\"lastName\":\"Saracco\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-05-08 10:02:47\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4388361/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4388361/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":78390985,\"identity\":\"a4f9acc6-a463-4667-8007-f6104c2b6960\",\"added_by\":\"auto\",\"created_at\":\"2025-03-12 18:16:23\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":901443,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"DiCo.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4388361/v1_covered_d7021cac-1da2-42fb-b614-8e8f1c45628e.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Verified authors shape X/Twitter discursive communities\",\"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\":\"info@researchsquare.com\",\"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\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4388361/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4388361/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCommunity detection algorithms try to extract a mesoscale structure from the available network data, generally avoiding any explicit assumption regarding the quantity and quality of information conveyed by specific sets of edges. 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