Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8857
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dc.contributor.authorBayrak, Cansin-
dc.contributor.authorKutlu, Mucahid-
dc.date.accessioned2022-11-30T19:22:08Z-
dc.date.available2022-11-30T19:22:08Z-
dc.date.issued2022-
dc.identifier.issn2329-924X-
dc.identifier.urihttps://doi.org/10.1109/TCSS.2022.3178052-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8857-
dc.descriptionArticle; Early Accessen_US
dc.description.abstractSocial media platforms provide massive amounts of data that can be used to analyze social issues and forecast events in the future. However, it is a challenging task due to the biased and noisy nature of the data. In this work, we propose a method to predict election results via Twitter. In particular, we first detect the stance of social media accounts using their retweets. Subsequently, we develop four different counting methods for our prediction task. In the simple user counting (SC) method, we count labeled users without taking any further steps to reduce bias. In the city-based weighted counting (CBWC) method, we apply a weighted counting based on the number of electorate in each city. The closest-city-based prediction (CCBP) method utilizes sociological similarity between cities to predict results for cities with limited sample sizes. The using former election results (UFERs) method compares predictions for each city against former election results to detect data bias and uses them accordingly. We evaluate our proposed methods with the data collected for the presidential election of Turkey held in 2018. In our extensive evaluation, we show that utilizing domain-specific information and location-based weighted counting is effective in reducing bias. CBWC, CCBP, and UFER methods outperform tweet-counting-based baseline methods. Furthermore, UFER and CCBP outperform almost all traditional polls, suggesting that social media platforms are alternative mediums for conducting election polls.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [ARDEB 3501, 120E514]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) ARDEB 3501 under Grant 120E514. The numerical calculations reported in this paper were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).en_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions On Computational Social Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElection predictionen_US
dc.subjectpublic opinionen_US
dc.subjectsocial mediaen_US
dc.subjectstance detectionen_US
dc.subjectTwitteren_US
dc.titlePredicting Election Results Via Social Media: a Case Study for 2018 Turkish Presidential Electionen_US
dc.typeArticleen_US
dc.authoridKutlu, Mucahid/0000-0002-5660-4992-
dc.authoridBAYRAK, CANSIN/0000-0003-0741-9501-
dc.identifier.wosWOS:000809362300001en_US
dc.identifier.scopus2-s2.0-85131770228en_US
dc.identifier.doi10.1109/TCSS.2022.3178052-
dc.authorscopusid57202773428-
dc.authorscopusid35299304300-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
dc.ozel2022v3_Editen_US
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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