Assessing the bias in samples of large online networks

Sandra González-Bailón, Ning Wang, Alejandro Rivero, Javier Borge-Holthoefer, Yamir Moreno

Research output: Contribution to journalArticle

89 Citations (Scopus)

Abstract

We consider the sampling bias introduced in the study of online networks when collecting data through publicly available APIs (application programming interfaces). We assess differences between three samples of Twitter activity; the empirical context is given by political protests taking place in May 2012. We track online communication around these protests for the period of one month, and reconstruct the network of mentions and re-tweets according to the search and the streaming APIs, and to different filtering parameters. We find that smaller samples do not offer an accurate picture of peripheral activity; we also find that the bias is greater for the network of mentions, partly because of the higher influence of snowballing in identifying relevant nodes. We discuss the implications of this bias for the study of diffusion dynamics and political communication through social media, and advocate the need for more uniform sampling procedures to study online communication.

Original languageEnglish
Pages (from-to)16-27
Number of pages12
JournalSocial Networks
Volume38
Issue number1
DOIs
Publication statusPublished - 1 Jul 2014

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Keywords

  • Graph comparison
  • Measurement error
  • Political communication
  • Social media
  • Social protests
  • Twitter

ASJC Scopus subject areas

  • Sociology and Political Science
  • Social Sciences(all)
  • Anthropology
  • Psychology(all)

Cite this

González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. (2014). Assessing the bias in samples of large online networks. Social Networks, 38(1), 16-27. https://doi.org/10.1016/j.socnet.2014.01.004