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The Morality Machine: Tracking Moral Values in Tweets

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Advances in Intelligent Data Analysis XV (IDA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9897))

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Abstract

This paper introduces The Morality Machine, a system that tracks ethical sentiment in Twitter discussions. Empirical approaches to ethics are rare, and to our knowledge this system is the first to take a machine learning approach. It is based on Moral Foundations Theory, a framework of moral values that are assumed to be universal. Carefully handcrafted keyword dictionaries for Moral Foundations Theory exist, but experiments demonstrate that models that do not leverage these have similar or superior performance, thus proving the value of a more pure machine learning approach.

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Author information

Authors and Affiliations

  1. Media Technology, Leiden University, Leiden, The Netherlands

    Livia Teernstra, Peter van der Putten & Fons Verbeek

  2. Faculty of Social Science, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Livia Teernstra & Liesbeth Noordegraaf-Eelens

Authors
  1. Livia Teernstra
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  2. Peter van der Putten
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  3. Liesbeth Noordegraaf-Eelens
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  4. Fons Verbeek
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Corresponding author

Correspondence to Livia Teernstra .

Editor information

Editors and Affiliations

  1. Stockholm University , Stockholm, Sweden

    Henrik Boström

  2. Leiden University , Leiden, The Netherlands

    Arno Knobbe

  3. University of Porto , Porto, Portugal

    Carlos Soares

  4. Stockholm University , Stockholm, Sweden

    Panagiotis Papapetrou

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Teernstra, L., van der Putten, P., Noordegraaf-Eelens, L., Verbeek, F. (2016). The Morality Machine: Tracking Moral Values in Tweets. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-46349-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46348-3

  • Online ISBN: 978-3-319-46349-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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