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Inferring Human Traits from Facebook Statuses

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Social Informatics (SocInfo 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11185))

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Abstract

This paper explores the use of language models to predict 20 human traits from users’ Facebook status updates. The data was collected by the myPersonality project, and includes user statuses along with their personality, gender, political identification, religion, race, satisfaction with life, IQ, self-disclosure, fair-mindedness, and belief in astrology. A single interpretable model meets state of the art results for well-studied tasks such as predicting gender and personality; and sets the standard on other traits such as IQ, sensational interests, political identity, and satisfaction with life. Additionally, highly weighted words are published for each trait. These lists are valuable for creating hypotheses about human behavior, as well as for understanding what information a model is extracting. Using performance and extracted features we analyze models built on social media. The real world problems we explore include gendered classification bias and Cambridge Analytica’s use of psychographic models.

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Authors and Affiliations

  1. Boston University, Boston, MA, USA

    Andrew Cutler & Brian Kulis

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  1. Andrew Cutler
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  2. Brian Kulis
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Correspondence to Andrew Cutler .

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Editors and Affiliations

  1. University of Koblenz, Koblenz, Germany

    Steffen Staab

  2. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

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Cutler, A., Kulis, B. (2018). Inferring Human Traits from Facebook Statuses. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11185. Springer, Cham. https://doi.org/10.1007/978-3-030-01129-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-01129-1_11

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