A Hybrid Model for Role-related User Classification on Twitter

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dc.contributor.authorLi, Liuqingen
dc.contributor.authorSong, Ziqianen
dc.contributor.authorZhang, Xuanen
dc.contributor.authorFox, Edward A.en
dc.contributor.departmentDigital Library Research Laboratoryen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2018-11-26T18:07:56Zen
dc.date.available2018-11-26T18:07:56Zen
dc.date.issued2018-11-15en
dc.description.abstractTo aid a variety of research studies, we propose TWIROLE, a hybrid model for role-related user classification on Twitter, which detects male-related, female-related, and brand-related (i.e., organization or institution) users. TWIROLE leverages features from tweet contents, user profiles, and profile images, and then applies our hybrid model to identify a user’s role. To evaluate it, we used two existing large datasets about Twitter users, and conducted both intra- and inter-comparison experiments. TWIROLE outperforms existing methods and obtains more balanced results over the several roles. We also confirm that user names and profile images are good indicators for this task. Our research extends prior work that does not consider brand-related users, and is an aid to future evaluation efforts relative to investigations that rely upon self-labeled datasets.en
dc.description.sponsorshipNSF: IIS-1619028en
dc.identifier.urihttp://hdl.handle.net/10919/86162en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/en
dc.subjectclassificationen
dc.subjecthybrid modelen
dc.subjectrole-relateden
dc.subjectTwitteren
dc.titleA Hybrid Model for Role-related User Classification on Twitteren
dc.typeArticleen

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