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

HARGITTAI, ESZTER Cappella, Joseph N ; Neuman, W. Russell ; Shah, Dhavan V

The Annals of the American Academy of Political and Social Science, 2015-05, Vol.659 (1), p.63-76 [Periódico revisado por pares]

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Título:
Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites
Autor: HARGITTAI, ESZTER
Cappella, Joseph N ; Neuman, W. Russell ; Shah, Dhavan V
Assuntos: Bias ; Data analysis ; Ethnicity ; Generalization ; Perspectives on Computational Social Science ; Sampling ; Social networks ; Socioeconomic status ; Survey data
É parte de: The Annals of the American Academy of Political and Social Science, 2015-05, Vol.659 (1), p.63-76
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: 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.
Editor: Los Angeles, CA: SAGE Publications
Idioma: Inglês

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