Modeling facebook posting life-cycle

Joaquim Castro-Fonseca, Antonio Grilo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As Facebook becomes a quite relevant tool for companies marketing and sales it is important to analyze and understand posting activity. Propagation of relevant episodes in Facebook is quite fast and companies must not only plan, monitor and control the posting activities in their own Facebook page but also understand what is happening in their competitors Facebook. This paper presents a model and algorithm that allows the implementation of automated monitoring of Facebook posting activity, identifying normal and outliers in their activity, and hence enhancing companies' Facebook competitive intelligence. The model is validated with a data sample of 27924 public publications from the 550 companies Facebook pages.

Original languageEnglish
Title of host publicationSmart Digital Futures 2014
PublisherIOS Press
Pages17-24
Number of pages8
Volume262
ISBN (Print)9781614994046
DOIs
Publication statusPublished - 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume262
ISSN (Print)09226389

Keywords

  • Facebook
  • Online Social Networks
  • Regression Model

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    Castro-Fonseca, J., & Grilo, A. (2014). Modeling facebook posting life-cycle. In Smart Digital Futures 2014 (Vol. 262, pp. 17-24). (Frontiers in Artificial Intelligence and Applications; Vol. 262). IOS Press. https://doi.org/10.3233/978-1-61499-405-3-17