Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

Flavio Figueiredo, Jussara M. Almeida, Yasuko Matsubara, Bruno Ribeiro, Christos Faloutsos

ECML-PKDD 2014 pp. 386-401

doi:10.1007/978-3-662-44848-9_25 /ecmlpkdd/2014/figueiredo2014ecmlpkdd-revisit/

Abstract

How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.

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Cite

Text

Figueiredo et al. "Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44848-9_25

Markdown

[Figueiredo et al. "Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/figueiredo2014ecmlpkdd-revisit/) doi:10.1007/978-3-662-44848-9_25

BibTeX

@inproceedings{figueiredo2014ecmlpkdd-revisit,
  title     = {{Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries}},
  author    = {Figueiredo, Flavio and Almeida, Jussara M. and Matsubara, Yasuko and Ribeiro, Bruno and Faloutsos, Christos},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2014},
  pages     = {386-401},
  doi       = {10.1007/978-3-662-44848-9_25},
  url       = {https://mlanthology.org/ecmlpkdd/2014/figueiredo2014ecmlpkdd-revisit/}
}