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dc.contributor.advisorChakrabarty, Amitabha
dc.contributor.authorDey, Amitabha
dc.contributor.authorRafi, Rafsan Zani
dc.contributor.authorHasan, Shahriar
dc.contributor.authorKundu, Sauvik
dc.date.accessioned2018-05-14T05:10:20Z
dc.date.available2018-05-14T05:10:20Z
dc.date.copyright2018
dc.date.issued2018-04
dc.identifier.otherID 13201070
dc.identifier.otherID 13201030
dc.identifier.otherID 14101233
dc.identifier.otherID 13201055
dc.identifier.urihttp://hdl.handle.net/10361/10139
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references.
dc.description.abstractThe upsurge of fake news in social media calls attention to the erosion of long-standing institutional defense against misinformation in this Digital Age. In the wake of the 2016 Presidential Election in US, where social media played a crucial role in swinging votes, fake news has been a subject of increased discussion and debate. Social media used for news consumption has both its perks and disadvantages. In one hand it is relatively inexpensive and can be easily accessed but at the same time the likelihood of falling prey to fake news cannot also be disregarded. In this paper, we initially examine some of the existing technologies and frameworks that have been adopted to augment humans to make better decisions when it comes to recognizing news deception. We perform veracity assessment; conduct a comprehensive linguistic analysis on tweets to extract bag-of-words to perform Classification, specially structured around a specific target, in an attempt to find noticeable pattern in reliable and untrustworthy news. We discuss several surveys that have been undertaken in the past to help us present a comprehensive review of detecting fake news on social media. We later turn to the discussion of interconnected research domains and future research directions for constructing an ideal model for fake news detection system around social media. Although designing a fake news detector is not a straightforward problem, we propose a head-down operational guideline for a feasible fake news detecting system from a linguistic perspective. Keywords - Deception, detection, social media, news verification, Bag of Words, linguistic analysis, semantic analysis, recognition, pattern, graph.en_US
dc.description.statementofresponsibilityAmitabha Dey
dc.description.statementofresponsibilityRafsan Zani Rafi
dc.description.statementofresponsibilityAmitabha Dey
dc.description.statementofresponsibilityRafsan Zani Rafi
dc.description.statementofresponsibilityShahriar Hasan Parash
dc.description.statementofresponsibilitySauvik Kundu Arko
dc.format.extent34 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.subjectFake newsen_US
dc.subjectPattern recognitionen_US
dc.subjectLinguistic analysisen_US
dc.titleFake news pattern recognition using linguistic analysisen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering 


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