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Predicting individual-level income from Facebook profiles

Author

Listed:
  • Sandra C Matz
  • Jochen I Menges
  • David J Stillwell
  • H Andrew Schwartz

Abstract

Information about a person’s income can be useful in several business-related contexts, such as personalized advertising or salary negotiations. However, many people consider this information private and are reluctant to share it. In this paper, we show that income is predictable from the digital footprints people leave on Facebook. Applying an established machine learning method to an income-representative sample of 2,623 U.S. Americans, we found that (i) Facebook Likes and Status Updates alone predicted a person’s income with an accuracy of up to r = 0.43, and (ii) Facebook Likes and Status Updates added incremental predictive power above and beyond a range of socio-demographic variables (ΔR2 = 6–16%, with a correlation of up to r = 0.49). Our findings highlight both opportunities for businesses and legitimate privacy concerns that such prediction models pose to individuals and society when applied without individual consent.

Suggested Citation

  • Sandra C Matz & Jochen I Menges & David J Stillwell & H Andrew Schwartz, 2019. "Predicting individual-level income from Facebook profiles," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-13, March.
    • Handle: RePEc:plo:pone00:0214369
    DOI: 10.1371/journal.pone.0214369
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    References listed on IDEAS

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    1. 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.
    2. Berthon, Pierre R. & Pitt, Leyland F. & Plangger, Kirk & Shapiro, Daniel, 2012. "Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy," Business Horizons, Elsevier, vol. 55(3), pages 261-271.
    3. Daniel Preoţiuc-Pietro & Svitlana Volkova & Vasileios Lampos & Yoram Bachrach & Nikolaos Aletras, 2015. "Studying User Income through Language, Behaviour and Affect in Social Media," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    4. Blau Francine D & Kahn Lawrence M, 2007. "The Gender Pay Gap," The Economists' Voice, De Gruyter, vol. 4(4), pages 1-6, June.
    5. William A. Darity & Patrick L. Mason, 1998. "Evidence on Discrimination in Employment: Codes of Color, Codes of Gender," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 63-90, Spring.
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    Cited by:

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    3. Min Liu & Sajid Anwar, 2024. "Analyzing horizontal integration and market efficiency in platform enterprises: A case study of exchanges," Economics and Politics, Wiley Blackwell, vol. 36(2), pages 1076-1089, July.

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