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

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  • Eszter Hargittai

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.

Suggested Citation

  • Eszter Hargittai, 2015. "Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 63-76, May.
    • Handle: RePEc:sae:anname:v:659:y:2015:i:1:p:63-76
    DOI: 10.1177/0002716215570866
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    References listed on IDEAS

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    1. Rajagopal, 2013. "Social Media Metrics," Palgrave Macmillan Books, in: Managing Social Media and Consumerism, chapter 7, pages 132-151, Palgrave Macmillan.
    2. Ira M. Wasserman & Marie Richmond‐Abbott, 2005. "Gender and the Internet: Causes of Variation in Access, Level, and Scope of Use," Social Science Quarterly, Southwestern Social Science Association, vol. 86(1), pages 252-270, March.
    3. H Andrew Schwartz & Johannes C Eichstaedt & Margaret L Kern & Lukasz Dziurzynski & Stephanie M Ramones & Megha Agrawal & Achal Shah & Michal Kosinski & David Stillwell & Martin E P Seligman & Lyle H U, 2013. "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
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    Cited by:

    1. Jisoo Sim & Patrick Miller, 2019. "Understanding an Urban Park through Big Data," IJERPH, MDPI, vol. 16(20), pages 1-16, October.
    2. Thomas Fujiwara & Karsten Müller & Carlo Schwarz, 2021. "The Effect of Social Media on Elections: Evidence from the United States," NBER Working Papers 28849, National Bureau of Economic Research, Inc.
    3. Thomas Fujiwara & Karsten Müller & Carlo Schwarz, 2021. "The Effect of Social Media on Elections: Evidence from the United States," NBER Working Papers 28849, National Bureau of Economic Research, Inc.
    4. Mortati, Marzia & Magistretti, Stefano & Cautela, Cabirio & Dell’Era, Claudio, 2023. "Data in design: How big data and thick data inform design thinking projects," Technovation, Elsevier, vol. 122(C).
    5. Stieglitz, Stefan & Mirbabaie, Milad & Ross, Björn & Neuberger, Christoph, 2018. "Social media analytics – Challenges in topic discovery, data collection, and data preparation," International Journal of Information Management, Elsevier, vol. 39(C), pages 156-168.

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