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Predicting individual-level income from Facebook profiles

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  • 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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    Cited by:

    1. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.

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