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      Predicting individual-level income from Facebook profiles

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          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 (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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          Most cited references14

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          Self-Reports in Organizational Research: Problems and Prospects

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            Singular value decomposition and least squares solutions

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              Facebook language predicts depression in medical records

              Significance Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be used to screen consenting individuals for depression. Further, social media content may point clinicians to specific symptoms of depression.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 March 2019
                2019
                : 14
                : 3
                : e0214369
                Affiliations
                [1 ] Columbia Business School, Columbia University, New York, NY, United States
                [2 ] Department of Business Administration, University of Zurich, Zurich, Switzerland
                [3 ] Judge Business School, University of Cambridge, Cambridge, United Kingdom
                [4 ] Department of Computer Science, Stony-Brook University, Stony Brook, NY, United States
                Tilburg University, NETHERLANDS
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-0969-4403
                Article
                PONE-D-18-19521
                10.1371/journal.pone.0214369
                6438464
                30921389
                1261e5c3-2c5a-4a71-98a0-9dd1d97665b1
                © 2019 Matz et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 July 2018
                : 12 March 2019
                Page count
                Figures: 3, Tables: 2, Pages: 13
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Facebook
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Facebook
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Facebook
                Social Sciences
                Economics
                Labor Economics
                Salaries
                Biology and Life Sciences
                Psychology
                Personality
                Social Sciences
                Psychology
                Personality
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                Social Sciences
                Sociology
                Communications
                Marketing
                Social Sciences
                Economics
                Finance
                Social Sciences
                Economics
                Labor Economics
                Employment
                Jobs
                People and Places
                Population Groupings
                Ethnicities
                Custom metadata
                Given the sensitive nature of our data, we cannot make them publicly available. Data can be requested from the corresponding author Sandra Matz ( sm4409@ 123456gsb.columbia.edu ) or the Cambridge Psychology Research Ethics Committee, School of the Biological Sciences University of Cambridge, 17 Mill Lane, Cambridge CB2 1RX ( Cheryl.Torbett@ 123456admin.cam.ac.uk ), for researchers who meet the criteria for access to confidential data.

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