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Automated Detection of Nostalgic Text in the Context of Societal Pessimism

  • Conference paper
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Disinformation in Open Online Media (MISDOOM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12021))

Abstract

In online media environments, nostalgia can be used as important ingredient of propaganda strategies, specifically, by creating societal pessimism. This work addresses the automated detection of nostalgic text as a first step towards automatically identifying nostalgia-based manipulation strategies. We compare the performance of standard machine learning approaches on this challenge and demonstrate the successful transfer of the best performing approach to real-world nostalgia detection in a case study.

The authors acknowledge support by the German Federal Ministry of Education and Research (FKZ 16KIS0495K) and the European Research Center for Information Systems (ERCIS) as well as the Digital Society research program funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia. Further, the authors thank Constantine Sedikides, Tim Wildschut, and Tim Wulf for their advice in designing the conducted data collection study.

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Notes

  1. 1.

    https://www.destatis.de.

  2. 2.

    As the classes of the classification problem are balanced, the accuracy metric can be used as a first indicator to compare classifier results.

  3. 3.

    http://www.spiegel.de/wissenschaft/mensch/erziehung-lasst-eure-kinder-frei-kolumne-a-1223770.html.

  4. 4.

    Median = 121 words, min = 61 words, max = 248 words.

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Authors and Affiliations

  1. Department of Information Systems, University of Münster, Leonardo-Campus 3, 48149, Münster, Germany

    Lena Clever, Heike Trautmann & Christian Grimme

  2. Department of Communication, University of Münster, Bispinghof 9-14, 48149, Münster, Germany

    Lena Frischlich

Authors
  1. Lena Clever
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  2. Lena Frischlich
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  3. Heike Trautmann
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  4. Christian Grimme
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Corresponding author

Correspondence to Lena Clever .

Editor information

Editors and Affiliations

  1. Institut für Wirtschaftsinformatik, WWU Münster, Münster, Nordrhein-Westfalen, Germany

    Christian Grimme

  2. LIACS, Leiden University, Leiden, Zuid-Holland, The Netherlands

    Mike Preuss

  3. LIACS, Leiden University, Leiden, Zuid-Holland, The Netherlands

    Frank W. Takes

  4. Department of Communication, University of Münster, Münster, Germany

    Annie Waldherr

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Clever, L., Frischlich, L., Trautmann, H., Grimme, C. (2020). Automated Detection of Nostalgic Text in the Context of Societal Pessimism. In: Grimme, C., Preuss, M., Takes, F., Waldherr, A. (eds) Disinformation in Open Online Media. MISDOOM 2019. Lecture Notes in Computer Science(), vol 12021. Springer, Cham. https://doi.org/10.1007/978-3-030-39627-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-39627-5_5

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  • Online ISBN: 978-3-030-39627-5

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