Is There Personalization in Twitter Search? A Study on polarized opinions about the Brazilian Welfare Reform

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dc.contributor.authorDos Santos, Jonatas C.
dc.contributor.authorSiqueira, S.
dc.contributor.authorPereira Nunes, Bernardo
dc.contributor.authorBalestrassi, Pedro Paulo
dc.contributor.authorPereira, Fabricio R. S.
dc.coverage.spatialSouthampton United Kingdom
dc.date.accessioned2024-01-22T03:40:37Z
dc.date.available2024-01-22T03:40:37Z
dc.date.createdJuly 6 - 10, 2020
dc.date.issued2020-07
dc.date.updated2022-10-02T07:17:23Z
dc.description.abstractPersonalization algorithms play an essential role in the way search platforms fetch results to users. While there are many empirical studies about the effects of these algorithms on Web searches like Google and Bing, reports about personalization on social media searches are rare. This exploratory study aims to understand and quantify the limits of personalization in Twitter search results. We developed a measurement methodology and agents to train a pair of polarized Twitter accounts and simultaneously collected search results from these accounts. The agents were run in a political context, the Brazilian Welfare Reform. Our findings show a significant amount of personalization differences when we compare search results from a new fresh profile to non-fresh ones. Peculiarly, little evidence for differences between two profiles that followed different accounts with polarized viewpoints about the same topic was found - the filter bubble hypothesis cannot be null.en_AU
dc.description.sponsorshipThis study was financed in part by the National Council for Scientific and Technical Development (CNPq) - Brazil, Grant 315374/2018-7.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.citationJônatas C. dos Santos, SeanW. M. Siqueira, Bernardo Pereira Nunes, Fabrício R. S. Pereira, and Pedro P. Balestrassi. 2020. Is There Personalization in Twitter Search? A Study on polarized opinions about the Brazilian Welfare Reform. In 12th ACM Conference on Web Science (WebSci ’20), July 6–10, 2020, Southampton, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3394231.3397917en_AU
dc.identifier.isbn978-1-4503-7989-2en_AU
dc.identifier.urihttp://hdl.handle.net/1885/311705
dc.language.isoen_AUen_AU
dc.provenanceclassroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.en_AU
dc.publisherAssociation for Computing Machinery (ACM)en_AU
dc.relation.ispartofseries12th ACM Conference on Web Scienceen_AU
dc.rights© 2020 Association for Computing Machineryen_AU
dc.sourceWebSci '20: 12th ACM Conference on Web Scienceen_AU
dc.subjectPersonalizationen_AU
dc.subjectTwitter Searchen_AU
dc.subjectSocial Media Searchen_AU
dc.titleIs There Personalization in Twitter Search? A Study on polarized opinions about the Brazilian Welfare Reformen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage276en_AU
local.bibliographicCitation.startpage267en_AU
local.contributor.affiliationDos Santos, Jonatas C., Federal University of the State of Rio de Janeiro (UNIRIO), Brazilen_AU
local.contributor.affiliationSiqueira, S., Federal University of the State of Rio de Janeiroen_AU
local.contributor.affiliationPereira Nunes, Bernardo, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationBalestrassi, Pedro Paulo, Federal University of Itajubá, Brazilen_AU
local.contributor.affiliationPereira, Fabricio R. S., Federal University of the State of Rio de Janeiro (UNIRIO), Brazilen_AU
local.contributor.authoremailu1064702@anu.edu.auen_AU
local.contributor.authoruidPereira Nunes, Bernardo, u1064702en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor460803 - Collaborative and social computingen_AU
local.identifier.absfor460506 - Graph, social and multimedia dataen_AU
local.identifier.ariespublicationa383154xPUB16864en_AU
local.identifier.doi10.1145/3394231.3397917en_AU
local.identifier.scopusID2-s2.0-85088385192
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://dl.acm.org/en_AU
local.type.statusPublished Versionen_AU

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