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Automated Classification of Potentially Insulting Speech Acts on Social Network Sites

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Digital Transformation and Global Society (DTGS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1503))

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Abstract

Insulting speech acts have become the subject of public discussion in the media, social media, the basis for speculation in political communication, and a working concept in the legal environment. The present research article explores insulting speech acts on the social network site “VKontakte” aiming to develop an algorithm for automatic classification of text data. We conducted semantic analysis of the text of “Article 5.61” of the Code of Administrative Offenses of the Russian Federation, which made it possible to formulate inclusion criteria for formal classification. We used three common word embeddings models (BERT, ELMo, and fastText) on the original Russian language dataset consisting of 4596 annotated messages perceived as insulting speech acts. General findings argue that even in a specialized dataset the share of messages that meet criteria of inclusion is negligible. This indicates a low probability of going to court on the fact of an administrative offense under Article 5.61 based on speech communication on social network sites, even though such communication is public in nature and is automatically recorded in writing. Machine learning text classifier based on BERT model showed best performance.

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Notes

  1. 1.

    https://huggingface.co/DeepPavlov/rubert-base-cased-conversational.

  2. 2.

    https://huggingface.co/sismetanin/rubert_conversational-ru-sentiment-rusentiment.

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Funding

The research done for this work has been supported by the 1st Workshop at the Mathematical Center in Akademgorodok (project No 26 “Mathematical support for linguistic expertise”, 13 July–14 August, 2020) http://mca.nsu.ru/workshopen/. The authors express their sincere gratitude to the students of the Engineering School of Novosibirsk State University, especially to M.V. Fedorova and E.V. Timofeeva, as well as a student of the Higher School of Economics M.O. Maslova, who made an invaluable contribution to the collection of the dataset and acted as annotators.

Author information

Authors and Affiliations

  1. Institute of Scientific Information for Social Sciences of the Russian Academy of Sciences, 51/21 Nakhimovsky Prospect, Moscow, 117418, Russia

    Liliya Komalova & Ekaterina Mayorova

  2. Moscow State Linguistic University, 38 Ostozhenka Str., Moscow, 119034, Russia

    Liliya Komalova & Leonid Motovskikh

  3. University of Tyumen, 6 Volodarskogo Str., Tyumen, 625003, Russia

    Anna Glazkova

  4. Novosibirsk State University, 1 Pirogova Str., Novosibirsk, 630090, Russia

    Dmitry Morozov & Rostislav Epifanov

Authors
  1. Liliya Komalova
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  2. Anna Glazkova
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  3. Dmitry Morozov
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  4. Rostislav Epifanov
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  5. Leonid Motovskikh
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  6. Ekaterina Mayorova
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Corresponding author

Correspondence to Liliya Komalova .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, St. Petersburg, Russia

    Daniel A. Alexandrov

  2. ITMO University, St. Petersburg, Russia

    Alexander V. Boukhanovsky

  3. ITMO University, St. Petersburg, Russia

    Andrei V. Chugunov

  4. National Research University Higher School of Economics, St. Petersburg, Russia

    Yury Kabanov

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. National Research University Higher School of Economics, St. Petersburg, Russia

    Ilya Musabirov

  7. National Research University Higher School of Economics, St. Petersburg, Russia

    Sergei Pashakhin

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Komalova, L., Glazkova, A., Morozov, D., Epifanov, R., Motovskikh, L., Mayorova, E. (2022). Automated Classification of Potentially Insulting Speech Acts on Social Network Sites. In: Alexandrov, D.A., et al. Digital Transformation and Global Society. DTGS 2021. Communications in Computer and Information Science, vol 1503. Springer, Cham. https://doi.org/10.1007/978-3-030-93715-7_26

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  • DOI: https://doi.org/10.1007/978-3-030-93715-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93714-0

  • Online ISBN: 978-3-030-93715-7

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