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Measuring Conceptual Incongruity from Text-Based Annotations

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Intelligent Human Computer Interaction (IHCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11278))

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Abstract

We propose a method for measuring the conceptual incongruity of a digital object using associated text meta-data. We show that this measure correlates well with empirical creativity ratings elicited from human subjects in laboratory settings. Extending our focus to online resources, we show that the predicted incongruity of a movie plot in the Movielens database is weakly correlated with users’ ratings for the movie, but strongly correlated with variability in ratings. Movies with incongruous plots appear to elicit much more polarized responses. Further, in domains where cognitive theories suggest users are likely to be looking for incongruity, e.g. humor, we show, using the Youtube Comedy Slam Dataset, that user ratings for comedy pieces are considerably well-predicted by their incongruity score. These evaluations provide convergent evidence for the validity of our incongruity measurement, and immediately present several direct application possibilities. We present a case example of including incongruity as a recommender system metric to diversify the set of suggestions made in response to user queries in ways that align with users’ natural curiosity.

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Notes

  1. 1.

    https://www.dropbox.com/sh/9t23yh96jm9zp4s/AACdtMtAE9gLV89GylODoq7ja?dl=0.

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Authors and Affiliations

  1. Department of Computer Science, IIT Kanpur, Kanpur, India

    Nisheeth Srivastava

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  1. Nisheeth Srivastava
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Correspondence to Nisheeth Srivastava .

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Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

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Srivastava, N. (2018). Measuring Conceptual Incongruity from Text-Based Annotations. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-04021-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04020-8

  • Online ISBN: 978-3-030-04021-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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