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Título: Predicting consumers engagement on Facebook based on what and how companies write
Autor(es): ROSAS QUEZADA, ERIKA S.
RAMIREZ DE LA ROSA, ADRIANA GABRIELA
VILLATORO TELLO, ESAU
Temas: Marca en redes sociales
Análisis de impacto
Procesamiento de datos
Características ingeniería
Procesamiento natural del lenguaje
Fecha: 2019
Editorial: Nueva York : Cornell University
Citation: arXiv.org Cornell University 2019
Resumen: Engaged 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.
URI: http://ilitia.cua.uam.mx:8080/jspui/handle/123456789/886
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