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Analyzing Connections Between User Attributes, Images, and Text

Abstract

This work explores the relationship between a person’s demographic/psychological traits (e.g., gender and personality) and self-identity images and captions. We use a dataset of images and captions provided by N ≈ 1350 individuals, and we automatically extract features from both the images and captions. We identify several visual and textual properties that show reliable relationships with individual differences between participants. The automated techniques presented here allow us to draw interesting conclusions from our data that would be difficult to identify manually, and these techniques are extensible to other large datasets. Additionally, we consider the task of predicting gender and personality using both single modality features and multimodal features. We show that a multimodal predictive approach outperforms purely visual methods and purely textual methods. We believe that our work on the relationship between user characteristics and user data has relevance in online settings, where users upload billions of images each day.

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Notes

  1. 1.

    This data was collected under IRB approval at UT Austin.

  2. 2.

    For prediction results, we use a slightly different version of dominance (Dominance = 0.76y + 0.32s), as formulated in Machajdik and Hanbury [59].

  3. 3.

    Available at https://code.google.com/archive/p/word2vec/.

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Acknowledgments

We would like to thank Samuel Gosling for helping with the dataset collection, Shibamouli Lahiri for providing the code to calculate readability features, and Steven R. Wilson for providing the code to implement the Mairesse et al. paper that we use for prediction comparison.

Funding

This material is based in part upon work supported by the National Science Foundation (#1344257), the John Templeton Foundation (#48503), the Michigan Institute for Data Science, and DARPA (grant #HR001117S0026-AIDA-FP-045). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation, the John Templeton Foundation, the Michigan Institute for Data Science, or DARPA.

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Correspondence to Laura Burdick.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Burdick, L., Mihalcea, R., Boyd, R.L. et al. Analyzing Connections Between User Attributes, Images, and Text. Cogn Comput (2020). https://doi.org/10.1007/s12559-019-09695-3

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Keywords

  • Personality
  • Gender
  • Natural language processing
  • Computer vision
  • Computational social science