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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Predicting individual-level income from Facebook profiles |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | PLoS ONE |
| Publisher | Public Library of Science (PLoS) |
| Geographical Reach | international |
| ISSN | 1932-6203 |
| Volume | 14 |
| Number | 3 |
| Page Range | e0214369 |
| Date | 2019 |
| Abstract Text | 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. |
| Free access at | DOI |
| Official URL | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0214369 |
| Digital Object Identifier | 10.1371/journal.pone.0214369 |
| Other Identification Number | merlin-id:17938 |
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