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Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28867
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dc.contributor.authorAjao, Oluwaseunen_UK
dc.contributor.authorBhowmik, Deepayanen_UK
dc.contributor.authorZargari, Shahrzaden_UK
dc.date.accessioned2019-02-27T01:04:14Z-
dc.date.available2019-02-27T01:04:14Z-
dc.date.issued2019en_UK
dc.identifier.urihttp://hdl.handle.net/1893/28867-
dc.description.abstractMessages posted to online social networks (OSN) causes a recent stir due to the intended spread of fake news or rumor. This work aims to understand and analyse the characteristics of fake news especially in relation to sentiments, for the automatic detection of fake news and rumors. Based on empirical observations, we propose a hypothesis that there exists a relation between fake messages or rumours and sentiments of the texts posted online. We verify our hypothesis by comparing with the state-of-the-art baseline text-only fake news detection methods that do not consider sentiments. We performed experiments on standard Twitter fake news dataset and show good improvements in detecting fake news or rumor posts.en_UK
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.relationAjao O, Bhowmik D & Zargari S (2019) Sentiment Aware Fake News Detection on Online Social Networks. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, 12.05.2019-17.05.2019. Piscataway, NJ, USA: IEEE, pp. 2507-2511. https://doi.org/10.1109/ICASSP.2019.8683170en_UK
dc.relation.ispartofseriesIEEE International Conference on Acoustics, Speech, and Signal Processing Proceedingsen_UK
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_UK
dc.titleSentiment Aware Fake News Detection on Online Social Networksen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2019-04-17en_UK
dc.identifier.doi10.1109/ICASSP.2019.8683170en_UK
dc.citation.issn2379-190Xen_UK
dc.citation.spage2507en_UK
dc.citation.epage2511en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.citation.btitleICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en_UK
dc.citation.conferencedates2019-05-12 - 2019-05-17en_UK
dc.citation.conferencelocationBrighton, UKen_UK
dc.citation.conferencename2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)en_UK
dc.citation.date17/04/2019en_UK
dc.citation.isbn978-1-4799-8131-1en_UK
dc.publisher.addressPiscataway, NJ, USAen_UK
dc.contributor.affiliationSheffield Hallam Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationSheffield Hallam Universityen_UK
dc.identifier.wtid1230240en_UK
dc.contributor.orcid0000-0003-1762-1578en_UK
dc.date.accepted2019-02-01en_UK
dcterms.dateAccepted2019-02-01en_UK
dc.date.filedepositdate2019-02-20en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionAMen_UK
local.rioxx.authorAjao, Oluwaseun|en_UK
local.rioxx.authorBhowmik, Deepayan|0000-0003-1762-1578en_UK
local.rioxx.authorZargari, Shahrzad|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2019-04-17en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2019-04-17en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2019-04-17|en_UK
local.rioxx.filenameFake_News_ICASSP_2019_camera_ready.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source978-1-4799-8131-1en_UK
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