Predicting consumers engagement on Facebook based on what and how companies write

Autor:	Rosas-Quezada, ��rika S., Ram��rez-de-la-Rosa, Gabriela, Villatoro-Tello, Esa��
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	Social and Information Networks (cs.SI) FOS: Computer and information sciences Computer Science - Social and Information Networks
Popis:	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. Accepted at LKE 2019
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97779626f1503cce31b7d82c8317df9a http://arxiv.org/abs/1909.09914 Zobrazit plný text záznamu