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Analyzing Connections Between User Attributes, Images, and Text

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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. This data was collected under IRB approval at UT Austin.

  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. 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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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Michigan, Bob and Betty Beyster Building, 2260 Hayward Street, Ann Arbor, MI, 48109-2121, USA

    Laura Burdick & Rada Mihalcea

  2. Department of Psychology, Lancaster University, Lancaster, LA1 4YF, UK

    Ryan L. Boyd

  3. Department of Psychology, The University of Texas at Austin, SEA 4.208, 108 E. Dean Keeton Stop A8000, Austin, TX, 78712-1043, USA

    James W. Pennebaker

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  1. Laura Burdick
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Correspondence to Laura Burdick.

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Burdick, L., Mihalcea, R., Boyd, R.L. et al. Analyzing Connections Between User Attributes, Images, and Text. Cogn Comput 13, 241–260 (2021). https://doi.org/10.1007/s12559-019-09695-3

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