Journal Articles

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

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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.

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Availability

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Detail Information

Series Title

THE ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE

Call Number

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Publisher

Thousand Oaks : Sage Publications., 2015

Collation

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Language

English

ISBN/ISSN

978-1-5063-1463-1

Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

Volume 659, May 2015, pages 63-77

Subject(s)

Internet skills
Digital inequality
Social network sites
Sampling frame

Specific Detail Info

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Statement of Responsibility

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