Classification of cyberbullying in social media using Natural Language Programming method

Classification of cyberbullying in social media using Natural Language Programming method | 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 Classification of cyberbullying in social media using Natural Language Programming method P Vivekananth, Navneet Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3990421/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 The evolution of internet describes the usage of Social Media (SM) which expands drastically. This involvement continues to increase in context with the current global epidemic since people frequently use SM platforms to vent their feelings. Similarly, the widespread adoption of SM sites like Facebook and Twitter by many organizations has raised the volume of essential individual input regarding the state of affairs, events, goods, and services. One of the major drawbacks that widespread SM usage is cyberbullying. The frequency of cyberbullying on SM platforms has raised serious concerns for people, organizations, and society at large. To minimize negative impacts due to cyberbullying on SM, early detection is essential. As a result, Sentiment Analysis (SA) utilizing Twitter data has gained prominence. Text analysis-related Natural Language Processing (NLP) and in Artificial Intelligence (AI) technologies have gained increased attention because of increasing demand for SM analysis. Moreover, the important fields utilized to proactively extract and train high-quality characteristics from low-level text involved is Machine Learning (ML). The Convolution Neural Network (CNN) models are Deep Learning (DL) methods to train data like text, picture, and video data. Improved text classification is achieved by preparing these data types using CNN as a persuasive method. To categorize the bullying text, this study discusses the Ensemble model that integrates Modified Term Frequency and Inverse Document Frequency (MTF-IDF), and Deep Neural Network (DNN) with sophisticated feature extraction approaches. From the text data, the cyberbullying feature patterns are extracted using the techniques of feature extraction. social media cyberbullying Term Frequency - Inverse document frequency (TF-IDF) Natural Language Processing Machine Learning Sentiment Analysis (SA) 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. 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-3990421","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275094121,"identity":"474201f2-554a-4074-8dea-63882c646f49","order_by":0,"name":"P Vivekananth","email":"data:image/png;base64,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","orcid":"","institution":"IIS Deemed to be University","correspondingAuthor":true,"prefix":"","firstName":"P","middleName":"","lastName":"Vivekananth","suffix":""},{"id":275094122,"identity":"a81900bb-e567-42f3-920a-c7affb4a639f","order_by":1,"name":"Navneet Sharma","email":"","orcid":"","institution":"IIS Deemed to be University","correspondingAuthor":false,"prefix":"","firstName":"Navneet","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2024-02-26 08:20:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3990421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3990421/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51952971,"identity":"04c7046a-ed58-49ba-a28a-0233058c9d04","added_by":"auto","created_at":"2024-03-04 13:40:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":584977,"visible":true,"origin":"","legend":"","description":"","filename":"ScholarVivekanandhanSCIPaperIDETECTINGCYBERBULLYINGINSOCIALMEDIAANNLPBASEDCLASSIFICATIONFRAMEWORK1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990421/v1_covered_c1cb8416-aee8-484f-bcb7-e81a6fd4e298.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Classification of cyberbullying in social media using Natural Language Programming method","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 media, cyberbullying, Term Frequency - Inverse document frequency (TF-IDF), Natural Language Processing, Machine Learning, Sentiment Analysis (SA)","lastPublishedDoi":"10.21203/rs.3.rs-3990421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3990421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe evolution of internet describes the usage of Social Media (SM) which expands drastically. This involvement continues to increase in context with the current global epidemic since people frequently use SM platforms to vent their feelings. Similarly, the widespread adoption of SM sites like Facebook and Twitter by many organizations has raised the volume of essential individual input regarding the state of affairs, events, goods, and services. One of the major drawbacks that widespread SM usage is cyberbullying. The frequency of cyberbullying on SM platforms has raised serious concerns for people, organizations, and society at large. To minimize negative impacts due to cyberbullying on SM, early detection is essential. As a result, Sentiment Analysis (SA) utilizing Twitter data has gained prominence. Text analysis-related Natural Language Processing (NLP) and in Artificial Intelligence (AI) technologies have gained increased attention because of increasing demand for SM analysis. Moreover, the important fields utilized to proactively extract and train high-quality characteristics from low-level text involved is Machine Learning (ML). The Convolution Neural Network (CNN) models are Deep Learning (DL) methods to train data like text, picture, and video data. Improved text classification is achieved by preparing these data types using CNN as a persuasive method. To categorize the bullying text, this study discusses the Ensemble model that integrates Modified Term Frequency and Inverse Document Frequency (MTF-IDF), and Deep Neural Network (DNN) with sophisticated feature extraction approaches. From the text data, the cyberbullying feature patterns are extracted using the techniques of feature extraction.\u003c/p\u003e","manuscriptTitle":"Classification of cyberbullying in social media using Natural Language Programming method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 17:14:11","doi":"10.21203/rs.3.rs-3990421/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"457cfe94-3900-43eb-8af8-4c0641b8555e","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-04T13:39:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-29 17:14:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3990421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3990421","identity":"rs-3990421","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}