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 |
Aparece en las colecciones: | Artículos |
Fichero | Descripción | Tamaño | Formato | |
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Predicting consumers engagement on Facebook.pdf | 5.5 MB | Adobe PDF | Visualizar/Abrir |
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