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Using Instagram Picture Features to Predict Users’ Personality

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Book cover MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9516))

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Abstract

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying these filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users manipulate the appearance of their pictures by applying filters to them. To investigate this relationship, we studied the relationship between picture features and personality traits. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., relevant to hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and these picture features. Based on our findings, we also show that personality traits can be accurately predicted. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.

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Notes

  1. 1.

    https://instagram.com/press/ (accessed: 08/07/2015).

  2. 2.

    Our preliminary results of this work can be found in [10].

  3. 3.

    HITs (Human Intelligence Tasks) represent the assignments a user has participated in on Amazon Mechanical Turk prior to this study.

  4. 4.

    http://www.mathworks.com/help/vision/index.html.

  5. 5.

    The magnitude of the correlations are commonly found in relationships between personality traits and social media trails (e.g., [2, 12, 13, 24, 25, 27]).

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Acknowledgments

This research is supported by the Austrian Science Fund (FWF): P25655; and by the EU FP7/’13-’16 through the PHENICX project, grant agreement no. 601166.

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Authors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University, Altenberger Str. 69, 4040, Linz, Austria

    Bruce Ferwerda, Markus Schedl & Marko Tkalcic

Authors
  1. Bruce Ferwerda
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  2. Markus Schedl
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  3. Marko Tkalcic
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Corresponding author

Correspondence to Bruce Ferwerda .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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Ferwerda, B., Schedl, M., Tkalcic, M. (2016). Using Instagram Picture Features to Predict Users’ Personality. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_71

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  • DOI: https://doi.org/10.1007/978-3-319-27671-7_71

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  • Publisher Name: Springer, Cham

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