Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites

Eszter Hargittai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

This article discusses methodological challenges of using big data that rely on specific sites and services as their sampling frames, focusing on social network sites in particular. It draws on survey data to show that people do not select into the use of such sites randomly. Instead, use is biased in certain ways yielding samples that limit the generalizability of findings. Results show that age, gender, race/ethnicity, socioeconomic status, online experiences, and Internet skills all influence the social network sites people use and thus where traces of their behavior show up. This has implications for the types of conclusions one can draw from data derived from users of specific sites. The article ends by noting how big data studies can address the shortcomings that result from biased sampling frames.

Original languageEnglish (US)
Pages (from-to)63-76
Number of pages14
JournalAnnals of the American Academy of Political and Social Science
Volume659
Issue number1
DOIs
StatePublished - May 15 2015

Keywords

  • Internet skills
  • biased sample
  • big data
  • digital inequality
  • sampling
  • sampling frame
  • social network sites

ASJC Scopus subject areas

  • Sociology and Political Science
  • Social Sciences(all)

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