Multimodal Analysis and Prediction of Latent User Dimensions

  • Laura Wendlandt
  • Rada Mihalcea
  • Ryan L. Boyd
  • James W. Pennebaker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)

Abstract

Humans upload over 1.8 billion digital images to the internet each day, yet the relationship between the images that a person shares with others and his/her psychological characteristics remains poorly understood. In the current research, we analyze the relationship between images, captions, and the latent demographic/psychological dimensions of personality and gender. We consider a wide range of automatically extracted visual and textual features of images/captions that are shared by a large sample of individuals (\(N \approx 1,350\)). Using correlational methods, we identify several visual and textual properties that show strong relationships with individual differences between participants. Additionally, we explore the task of predicting user attributes using a multimodal approach that simultaneously leverages images and their captions. Results from these experiments suggest that images alone have significant predictive power and, additionally, multimodal methods outperform both visual features and textual features in isolation when attempting to predict individual differences.

Keywords

Analysis of latent user dimensions Multimodal prediction Joint language/vision models 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Laura Wendlandt
    • 1
  • Rada Mihalcea
    • 1
  • Ryan L. Boyd
    • 2
  • James W. Pennebaker
    • 2
  1. 1.University of MichiganAnn ArborUSA
  2. 2.University of Texas at AustinAustinUSA

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