Facebook wall posts: a model of user behaviors

  • Pravallika Devineni
  • Danai Koutra
  • Michalis Faloutsos
  • Christos Faloutsos
Original Article

DOI: 10.1007/s13278-017-0422-9

Cite this article as:
Devineni, P., Koutra, D., Faloutsos, M. et al. Soc. Netw. Anal. Min. (2017) 7: 6. doi:10.1007/s13278-017-0422-9

Abstract

How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friendship graph or are limited by the amount of users or the duration of the study. In this work, we model Facebook user behavior: we analyze the wall activities of users focusing on identifying common patterns and surprising phenomena. We conduct an extensive study of roughly 7k users over 3 years during 4-month intervals each year. We propose PowerWall, a lesser known heavy-tailed distribution to fit our data. Our key results can be summarized in the following points. First, we find that many wall activities, including number of posts, number of likes, number of posts of type photo, can be described by the PowerWall distribution. What is more surprising is that most of these distributions have similar slope, with a value close to 1! Second, we show how our patterns and metrics can help us spot surprising behaviors and anomalies. For example, we find a user posting every two days, exactly the same count of posts; another user posting at midnight, with no other activity before or after. Our work provides a solid step toward a systematic and quantitative wall-centric profiling of Facebook user activity.

Keywords

Heavy-tailed distributions Power law Social networks Facebook User behavior Statistical modeling 

Funding information

Funder NameGrant NumberFunding Note
National Science Foundation
  • SaTC 1314935
  • CNS 1314632
National Science Foundation
  • IIS 1217559
  • IIS 1408924
Defense Advanced Research Projects Agency
  • W911NF-12-C-0028
  • W911NF-09-2-0053

Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  1. 1.Department of Computer Science, Bourns School of EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborUSA
  3. 3.Department of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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