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dc.contributor.authorZhang, Qiang
dc.contributor.authorYilmaz, Emine
dc.contributor.authorLiang, Shangsong
dc.date.accessioned2018-04-24T06:46:18Z
dc.date.available2018-04-24T06:46:18Z
dc.date.issued2018-04-18
dc.identifier.citationZhang Q, Yilmaz E, Liang S (2018) Ranking-based Method for News Stance Detection. Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18. Available: http://dx.doi.org/10.1145/3184558.3186919.
dc.identifier.doi10.1145/3184558.3186919
dc.identifier.urihttp://hdl.handle.net/10754/627607
dc.description.abstractA valuable step towards news veracity assessment is to understand stance from different information sources, and the process is known as the stance detection. Specifically, the stance detection is to detect four kinds of stances (
dc.description.sponsorshipThis research was supported by the China Scholarship Council (CSC) for award No. 201704910908.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3184558.3186919
dc.rightsThis paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.
dc.subjectFake news
dc.subjectstance detection
dc.subjectlearning to rank
dc.titleRanking-based Method for News Stance Detection
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalCompanion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity College London, London, United Kingdom
kaust.personLiang, Shangsong
refterms.dateFOA2018-06-14T04:25:28Z
dc.date.published-online2018-04-18
dc.date.published-print2018


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