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Abusive Comments in Online Media and How to Fight Them

State of the Domain and a Call to Action

  • Conference paper
  • First Online:
Disinformation in Open Online Media (MISDOOM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12259))

Abstract

While abusive language in online contexts is a long-known problem, algorithmic detection and moderation support are only recently experiencing rising interest. This survey provides a structured overview of the latest academic publications in the domain. Assessed concepts include the used datasets, their language, annotation origins and quality, as well as applied machine learning approaches. It is rounded off by an assessment of meta aspects such as author collaborations and networks as well as extant funding opportunities. Despite all progress, the domain still has the potential to improve on many aspects: (international) collaboration, diversifying and increasing available datasets, careful annotations, and transparency. Furthermore, abusive language detection is a topic of high societal relevance and requires increased funding from public authorities.

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Notes

  1. 1.

    We are aware that there are multiple terms and concepts, such as “abusive language”, “hate speech”, “offensive language”, and many more. For this publication, we will use the term “abusive language”, as it receives increasing acceptance in the domain (cf., the “Workshop on Abusive Language Online” conducted annually) and is sufficiently generic to account for a multitude of equally problematic types of language.

  2. 2.

    The authors are interestingly not linked to the other Italian author cluster, indicating a somewhat lacking national collaboration.

  3. 3.

    Each recombined dataset (combined of \(n\ge 2\) already existent ones) is considered a novel dataset of its own right.

  4. 4.

    Datasets with all annotation strategies would have to be subjected to a testbed of multiple classifiers to ensure the improved performance is due to the chosen strategy.

  5. 5.

    For commonly used measures such as Krippendorff’s alpha, this should be around 0.8 or higher [22]. However, other measures can have different scales.

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Acknowledgments

The research leading to these results received funding from the federal state of North Rhine-Westphalia and the European Regional Development Fund (EFRE.NRW 2014–2020), Project: (No. CM-2-2-036a).

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Authors and Affiliations

  1. University of Münster – ERCIS, Leonardo-Campus 3, 48149, Münster, Germany

    Marco Niemann, Jens Welsing, Dennis M. Riehle, Jens Brunk, Dennis Assenmacher & Jörg Becker

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  1. Marco Niemann
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  3. Dennis M. Riehle
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Correspondence to Marco Niemann .

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Editors and Affiliations

  1. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Max van Duijn

  2. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Mike Preuss

  3. School of Politics and International Studies, University of Leeds, Leeds, UK

    Viktoria Spaiser

  4. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Frank Takes

  5. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Suzan Verberne

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Niemann, M., Welsing, J., Riehle, D.M., Brunk, J., Assenmacher, D., Becker, J. (2020). Abusive Comments in Online Media and How to Fight Them. In: van Duijn, M., Preuss, M., Spaiser, V., Takes, F., Verberne, S. (eds) Disinformation in Open Online Media. MISDOOM 2020. Lecture Notes in Computer Science(), vol 12259. Springer, Cham. https://doi.org/10.1007/978-3-030-61841-4_9

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  • DOI: https://doi.org/10.1007/978-3-030-61841-4_9

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