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dc.contributor.advisorParvez, Mohammad Zavid
dc.contributor.advisorKabir, Ashad
dc.contributor.advisorAkhond, Mostafijur Rahman
dc.contributor.authorShondhy, Sumaiya Islam
dc.contributor.authorKhan, Forhad Ahmed
dc.contributor.authorIbrahim, Syed Shoaib
dc.contributor.authorBarua, Shuvajit
dc.date.accessioned2021-10-19T06:07:51Z
dc.date.available2021-10-19T06:07:51Z
dc.date.copyright2021
dc.date.issued2021-01
dc.identifier.otherID 17101532
dc.identifier.otherID 17301083
dc.identifier.otherID 17301144
dc.identifier.otherID 17301168
dc.identifier.urihttp://hdl.handle.net/10361/15432
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 55-56).
dc.description.abstractIn this era of developed information and technology, any sort of information runs faster than air. The reliability of the information can be tricky at times. Some news publishing sources can publish news that are actually misguiding. The drastic evolution of electronic media over the past couple of decades has fueled the spread of fake news causing confusion and misunderstanding among the mass regarding any topic. The main motive behind producing these fake news is to create an agenda or to spread trepidation among people. People tend to become more panicked during any kind of disaster or pandemic, this it is easier to make them believe these misinformation in these times. Likewise, COVID-19 pandemic is not out of the grasp of misinformation spreading. To tackle this, we have proposed a Fake News Prediction model that will be used to detect fake news regarding COVID-19 that are being circulated in different electronic media.en_US
dc.description.statementofresponsibilitySumaiya Islam Shondhy
dc.description.statementofresponsibilityForhad Ahmed Khan
dc.description.statementofresponsibilitySyed Shoaib Ibrahim
dc.description.statementofresponsibilityShuvajit Barua
dc.format.extent56 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectCOVID-19en_US
dc.subjectFake news detectionen_US
dc.subjectDataseten_US
dc.subjectnews articleen_US
dc.subject.lcshCOVID-19 (Disease)
dc.titleCOVID-19 related fake news detection modelen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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