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Predicting individual-level income from Facebook profiles


Matz, Sandra C; Menges, Jochen I; Stillwell, David J; Schwartz, H Andrew (2019). Predicting individual-level income from Facebook profiles. PLoS ONE, 14(3):e0214369.

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 withan 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 ($ΔR^2$ =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.

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 withan 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 ($ΔR^2$ =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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Life Sciences > General Biochemistry, Genetics and Molecular Biology
Life Sciences > General Agricultural and Biological Sciences
Health Sciences > Multidisciplinary
Language:English
Date:28 March 2019
Deposited On:08 Aug 2019 08:11
Last Modified:30 May 2020 10:58
Publisher:Public Library of Science (PLoS)
ISSN:1932-6203
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1371/journal.pone.0214369
Official URL:https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0214369
Other Identification Number:merlin-id:17938

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