Reactions to Science Communication: Social Network Topic Using Word Embeddings and Semantic Knowledge

Reactions to Science Communication: Social Network Topic Using Word Embeddings and Semantic Knowledge | 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 Reactions to Science Communication: Social Network Topic Using Word Embeddings and Semantic Knowledge Bernardo Cerqueira de Lima, Renata Maria Abrantes Baracho, Thomas Mandl, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3097961/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Sep, 2023 Read the published version in Social Network Analysis and Mining → Version 1 posted 9 You are reading this latest preprint version Abstract Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources. This study aims to gain insights into how the public perceives scientific information, and how their behavior towards science communication (e.g. through videos or texts) during the pandemic is related to their information-seeking behavior. To analyze public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators, or representatives of research institutes, and analyzed their replies for 2 years from the start of the pandemic. One large challenge consists in sifting through social media data to find comments related to a reaction towards scientific content that might prove useful feedback to a content creator. The study aimed in developing a solution powered by topic modelling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. This architecture can be replicated for finding any documents related to niche topics in social media data. pandemic topic modelling machine learning communication Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Sep, 2023 Read the published version in Social Network Analysis and Mining → Version 1 posted Editorial decision: Major revision 28 Jul, 2023 Reviews received at journal 21 Jul, 2023 Reviews received at journal 09 Jul, 2023 Reviewers agreed at journal 09 Jul, 2023 Reviewers agreed at journal 03 Jul, 2023 Reviewers invited by journal 30 Jun, 2023 Editor assigned by journal 28 Jun, 2023 Submission checks completed at journal 23 Jun, 2023 First submitted to journal 22 Jun, 2023 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-3097961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":212435484,"identity":"27cb7605-95d3-42e1-b211-eb6dd3558490","order_by":0,"name":"Bernardo Cerqueira de Lima","email":"","orcid":"","institution":"Federal University of Minas Gerais, UFMG","correspondingAuthor":false,"prefix":"","firstName":"Bernardo","middleName":"Cerqueira","lastName":"de Lima","suffix":""},{"id":212435486,"identity":"b30cb2cc-c077-4365-ba55-bfc0e45d26a8","order_by":1,"name":"Renata Maria Abrantes Baracho","email":"","orcid":"","institution":"Federal University of Minas Gerais, UFMG","correspondingAuthor":false,"prefix":"","firstName":"Renata","middleName":"Maria Abrantes","lastName":"Baracho","suffix":""},{"id":212435487,"identity":"dadee87f-9dd3-4f8b-adc8-1ab3e38e708d","order_by":2,"name":"Thomas Mandl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3OsYrCMBjA8a9Lb6nObvcEQkRQQbm+yvch6CIuQnFwKBwXFx9B7l6h04FbSkGXWtds6uLUoW+gCYrgkKKbQ/4E8gXyIwGw2d602mEK4IIH4uACMHBCfSwnmN4IPk2I680DuBJdCanPt7Gk32RchUosMOj57Y/vnwiCnpG00nG/Q//JxIUqCswGtFrEXEI2MBMxatUUIf6ZM0E8QSaJS4cnZrLLFVkqAp4mZ5/tj5qczUTqV8I7EU6k7qslSkje7OB6eCWY9SlK1cfUUPKxUUMWsy79hV6zKIIvn202J6kGIzGErwKbzWazPXQBdz1e/7cPSL8AAAAASUVORK5CYII=","orcid":"","institution":"University of Hildesheim","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Mandl","suffix":""},{"id":212435488,"identity":"66fd6c9c-2c2f-49ab-9eb6-adebec5bcb81","order_by":3,"name":"Patricia Baracho Porto","email":"","orcid":"","institution":"Pontifical Catholic University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"Baracho","lastName":"Porto","suffix":""}],"badges":[],"createdAt":"2023-06-22 21:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3097961/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3097961/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13278-023-01125-5","type":"published","date":"2023-09-22T15:01:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43640701,"identity":"7b47134f-0484-4879-947d-1964f53c25ce","added_by":"auto","created_at":"2023-09-25 15:08:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":447185,"visible":true,"origin":"","legend":"","description":"","filename":"TopicModelingAssistedBySemantics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3097961/v1_covered_ce379533-fdf5-45de-bbd9-1eacb8949a50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reactions to Science Communication: Social Network Topic Using Word Embeddings and Semantic Knowledge","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"social-network-analysis-and-mining","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"snam","sideBox":"Learn more about [Social Network Analysis and Mining](http://link.springer.com/journal/13278)","snPcode":"13278","submissionUrl":"https://submission.nature.com/new-submission/13278/3","title":"Social Network Analysis and Mining","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"pandemic, topic modelling, machine learning, communication","lastPublishedDoi":"10.21203/rs.3.rs-3097961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3097961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. 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