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dc.contributor.authorROSAS QUEZADA, ERIKA S.-
dc.contributor.authorRAMIREZ DE LA ROSA, ADRIANA GABRIELA-
dc.contributor.authorVILLATORO TELLO, ESAU-
dc.coverage.spatial<dc:creator id="info:eu-repo/dai/mx/cvu/239516">ADRIANA GABRIELA RAMIREZ DE LA ROSA</dc:creator>-
dc.coverage.spatial<dc:creator id="info:eu-repo/dai/mx/cvu/165545">ESAU VILLATORO TELLO</dc:creator>-
dc.coverage.temporal<dc:subject>info:eu-repo/classification/cti/7</dc:subject>-
dc.date.accessioned2021-07-26T20:24:18Z-
dc.date.available2021-07-26T20:24:18Z-
dc.date.issued2019-
dc.identifier.citationarXiv.org Cornell University 2019en_US
dc.identifier.urihttp://ilitia.cua.uam.mx:8080/jspui/handle/123456789/886-
dc.description.abstractEngaged costumers are a very import part of current social media marketing. Public figures and brands have to be very careful about what to post online. That is why the need for accurate strategies for anticipating the impact of a post written for an online audience is critical to any public brand. Therefore, in this paper, we propose a method to predict the impact of a given post by accounting for the content, style, and behavioral attributes as well as metadata information. For validating our method we collected Facebook posts from 10 public pages, we performed experiments with almost 14000 posts and found that the content and the behavioral attributes from posts provide relevant information to our prediction model.en_US
dc.description.sponsorshipCornell Universityen_US
dc.language.isoInglésen_US
dc.publisherNueva York : Cornell Universityen_US
dc.rightshttps://arxiv.org/pdf/1909.09914.pdf-
dc.subjectMarca en redes socialesen_US
dc.subjectAnálisis de impactoen_US
dc.subjectProcesamiento de datosen_US
dc.subjectCaracterísticas ingenieríaen_US
dc.subjectProcesamiento natural del lenguajeen_US
dc.titlePredicting consumers engagement on Facebook based on what and how companies writeen_US
dc.typeArtículoen_US
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