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Enriching social analytics with latent Twitter image information

dc.creatorRazis G., Theofilou G., Anagnostopoulos I.en
dc.date.accessioned2023-01-31T09:51:21Z
dc.date.available2023-01-31T09:51:21Z
dc.date.issued2020
dc.identifier10.1109/SMAP49528.2020.9248464
dc.identifier.isbn9781728159195
dc.identifier.urihttp://hdl.handle.net/11615/78486
dc.description.abstractIn this paper, we propose a framework that uses latent information from Twitter images by employing the Google Cloud Vision API platform aiming at enriching social analytics with semantics and textual information. Our study reveals that user-generated content, linked data as well as hidden concepts and textual information from social images can be highly considered for enriching social analytics. Finally, we publish our annotated dataset for further use and evaluation from our researchcommunity. © 2020 IEEE.en
dc.language.isoenen
dc.sourceSMAP 2020 - 15th International Workshop on Semantic and Social Media Adaptation and Personalizationen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097624457&doi=10.1109%2fSMAP49528.2020.9248464&partnerID=40&md5=f78d38b32d84593c538024790b22c9b3
dc.subjectConcentration (process)en
dc.subjectSemanticsen
dc.subjectImage informationen
dc.subjectLatent informationen
dc.subjectSocial imagesen
dc.subjectTextual informationen
dc.subjectUser-generated contenten
dc.subjectSocial networking (online)en
dc.subjectInstitute of Electrical and Electronics Engineers Inc.en
dc.titleEnriching social analytics with latent Twitter image informationen
dc.typeconferenceItemen


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