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Network Analysis of German COVID-19 Related Discussions on Telegram

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Natural Language Processing and Information Systems (NLDB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13286))

Abstract

We present an effective way to create a dataset from relevant channels and groups of the messenger service Telegram, to detect clusters in this network, and to find influential actors. Our focus lies on the network of German COVID-19 sceptics that formed on Telegram along with growing restrictions meant to prevent the spreading of COVID-19. We create the dataset by using a scraper based on exponential discriminative snowball sampling, combining two different approaches. We show the best way to define a starting point for the sampling and to detect relevant neighbouring channels for the given data. Community clusters in the network are detected by using the Louvain method. Furthermore, we show influential channels and actors by defining a PageRank based ranking scheme. A heatmap illustrates the correlation between the number of channel members and the ranking. We also examine the growth of the network in relation to the governmental COVID-19 measures.

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Notes

  1. 1.

    https://telegram.org/.

  2. 2.

    https://github.com/th3unkn0n/TeleGram-Scraper.

  3. 3.

    https://github.com/PeterWalchhofer/Telescrape.

  4. 4.

    https://github.com/vali101/telegraph.

  5. 5.

    https://pypi.org/project/Telethon/.

  6. 6.

    https://app.querdenken-711.de/initiatives-directory.

  7. 7.

    https://python-louvain.readthedocs.io/en/latest/api.html.

  8. 8.

    E.g., that “the world is run by Satan-worshipping pedophiles” [11].

  9. 9.

    https://polyglot.readthedocs.io/en/latest/Detection.html.

  10. 10.

    In the dataset, about 8000 invite links to private channels were found.

  11. 11.

    School/workplace closing, stay at home requirements, travel restrictions, etc.

References

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Acknowledgement

figure a

The project on which this report is based was funded by the German Federal Ministry of Education and Research (BMBF) under the funding code 01|S20049. The author is responsible for the content of this publication.

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Authors and Affiliations

  1. University of Passau, Innstraße 41, 94032, Passau, Germany

    Valentin Peter, Ramona Kühn, Jelena Mitrović, Michael Granitzer & Hannah Schmid-Petri

Authors
  1. Valentin Peter
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  2. Ramona Kühn
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  3. Jelena Mitrović
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  4. Michael Granitzer
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  5. Hannah Schmid-Petri
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Corresponding author

Correspondence to Ramona Kühn .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de València, Valencia, Spain

    Paolo Rosso

  2. University of Turin, Torino, Italy

    Valerio Basile

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Raquel Martínez

  4. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  5. University of Derby, Derby, UK

    Farid Meziane

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Peter, V., Kühn, R., Mitrović, J., Granitzer, M., Schmid-Petri, H. (2022). Network Analysis of German COVID-19 Related Discussions on Telegram. In: Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2022. Lecture Notes in Computer Science, vol 13286. Springer, Cham. https://doi.org/10.1007/978-3-031-08473-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-08473-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08472-0

  • Online ISBN: 978-3-031-08473-7

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