Multilingual User Perceptions Analysis from Twitter using Zero Shot Learning for Border Control Technologies

Multilingual User Perceptions Analysis from Twitter using Zero Shot Learning for Border Control Technologies | 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 Multilingual User Perceptions Analysis from Twitter using Zero Shot Learning for Border Control Technologies Sarang Shaikh, Sule Yildirim Yayilgan, Erjon Zoto, Mohamed Abomhara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5269373/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Social Network Analysis and Mining → Version 1 posted 13 You are reading this latest preprint version Abstract Online social networks (OSNs) such as Twitter, Facebook, Instagram, and Reddit have transformed communication by enabling users to share opinions and perceptions on various topics. The vast amount of user-generated content on these platforms poses significant challenges for manual analysis. Advances in artificial intelligence, particularly transformer-based models like BERT and GPT, have improved the processing of multilingual data for tasks such as text classification, sentiment analysis, and emotion analysis. However, these models often require extensive task-specific training and high-quality labeled data, which is impractical for multilingual contexts. This study addresses these limitations by utilizing zero-shot learning (ZSL) with transformer-based models, which eliminate the need for task-specific training and can classify new data into unseen classes without manual annotation. The use case for this study is border control technologies (BCTs), a hot topic following the European Union (EU) commission’s ”Smart Borders Package” aimed at improving border crossing points’ efficiency and security. We introduce a novel ”user perception extraction architecture” to analyze multilingual perceptions of BCTs from Twitter. As there is no existing multilingual Twitter dataset for this purpose, we compiled a dataset of 90,789 multilingual tweets related to BCTs from 2008 to 2022. This study contributes to the research domain of user perception analysis from OSNs and opens new directions for understanding and improving global public perceptions of BCTs and other technologies or domains. online social networks transformer based models zero shot learning multilingual data analysis user perception extraction border control technologies Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Social Network Analysis and Mining → Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 10 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviews received at journal 29 Nov, 2024 Reviewers agreed at journal 19 Nov, 2024 Reviewers agreed at journal 16 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviews received at journal 13 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 30 Oct, 2024 Editor assigned by journal 24 Oct, 2024 Submission checks completed at journal 16 Oct, 2024 First submitted to journal 15 Oct, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5269373","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366794159,"identity":"2bf06129-a77c-4517-8d25-be54e73c167f","order_by":0,"name":"Sarang Shaikh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDADAwbGZmaGCtK1nGGQIEULAzMzYxsRWuTbzz78XPCLwd6c/XCzceG8O3XmEglsDz7gM/xMurH0zD6GxJ09ic3JM7c9k7CckcBuOAOve9IYpHl7GBIMDiQ2H+bddljC4EYCmzQPPof1P2P+DdRib3D+IVDLHCK0MNxIAyr4wcC44QbQYbwNRGgxuPGMzZq3QSJxw42HzcY8xw5LbjjzsE0Sn1/k+9OYb/P8sQE6LP2xNE/NYX6D48nHJPCFGBgwtqHEBmMDIQ1A8IcINaNgFIyCUTByAQDX0UqiNIf3IQAAAABJRU5ErkJggg==","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Sarang","middleName":"","lastName":"Shaikh","suffix":""},{"id":366794160,"identity":"1d4b9113-3d63-47c4-93ac-4ce7b8b61a45","order_by":1,"name":"Sule Yildirim Yayilgan","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sule","middleName":"Yildirim","lastName":"Yayilgan","suffix":""},{"id":366794161,"identity":"8825d021-3672-41db-a154-e7ae172c6284","order_by":2,"name":"Erjon Zoto","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Erjon","middleName":"","lastName":"Zoto","suffix":""},{"id":366794162,"identity":"2c97f8a2-c67d-4904-9dab-80979800244c","order_by":3,"name":"Mohamed Abomhara","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Abomhara","suffix":""}],"badges":[],"createdAt":"2024-10-15 14:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5269373/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5269373/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13278-025-01434-x","type":"published","date":"2025-03-18T15:58:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79120553,"identity":"3d3dd3d9-3128-4b6b-ba68-5215906cee98","added_by":"auto","created_at":"2025-03-24 16:09:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2565679,"visible":true,"origin":"","legend":"","description":"","filename":"MultilingualUserPerceptionsAnalysisfromOSNspaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5269373/v1_covered_da67f0cf-c9e2-4f02-b349-1001819f5c2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multilingual User Perceptions Analysis from Twitter using Zero Shot Learning for Border Control Technologies","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":"online social networks, transformer based models, zero shot learning, multilingual data analysis, user perception extraction, border control technologies","lastPublishedDoi":"10.21203/rs.3.rs-5269373/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5269373/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOnline social networks (OSNs) such as Twitter, Facebook, Instagram, and Reddit have transformed communication by enabling users to share opinions and perceptions on various topics. The vast amount of user-generated content on these platforms poses significant challenges for manual analysis. Advances in artificial intelligence, particularly transformer-based models like BERT and GPT, have improved the processing of multilingual data for tasks such as text classification, sentiment analysis, and emotion analysis. However, these models often require extensive task-specific training and high-quality labeled data, which is impractical for multilingual contexts. This study addresses these limitations by utilizing zero-shot learning (ZSL) with transformer-based models, which eliminate the need for task-specific training and can classify new data into unseen classes without manual annotation. The use case for this study is border control technologies (BCTs), a hot topic following the European Union (EU) commission’s ”Smart Borders Package” aimed at improving border crossing points’ efficiency and security. We introduce a novel ”user perception extraction architecture” to analyze multilingual perceptions of BCTs from Twitter. As there is no existing multilingual Twitter dataset for this purpose, we compiled a dataset of 90,789 multilingual tweets related to BCTs from 2008 to 2022. This study contributes to the research domain of user perception analysis from OSNs and opens new directions for understanding and improving global public perceptions of BCTs and other technologies or domains.\u003c/p\u003e","manuscriptTitle":"Multilingual User Perceptions Analysis from Twitter using Zero Shot Learning for Border Control Technologies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-24 11:37:35","doi":"10.21203/rs.3.rs-5269373/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-10T15:24:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-10T09:17:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-05T09:04:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-30T02:50:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111248446642856517872880380826840513787","date":"2024-11-19T05:56:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212862132932400334801355125933828680627","date":"2024-11-16T12:33:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228069935260871722893515305904299941897","date":"2024-11-14T01:53:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-14T01:27:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51536078747371145344400010416345584357","date":"2024-11-13T12:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-30T14:26:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-24T21:31:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-16T13:07:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Social Network Analysis and Mining","date":"2024-10-15T13:57:43+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"ad72b284-7462-4c7b-a397-52347104926f","owner":[],"postedDate":"October 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:04:41+00:00","versionOfRecord":{"articleIdentity":"rs-5269373","link":"https://doi.org/10.1007/s13278-025-01434-x","journal":{"identity":"social-network-analysis-and-mining","isVorOnly":false,"title":"Social Network Analysis and Mining"},"publishedOn":"2025-03-18 15:58:21","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-10-24 11:37:35","video":"","vorDoi":"10.1007/s13278-025-01434-x","vorDoiUrl":"https://doi.org/10.1007/s13278-025-01434-x","workflowStages":[]},"version":"v1","identity":"rs-5269373","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5269373","identity":"rs-5269373","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}