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Differences between antisemitic and non-antisemitic English language tweets

Author

Listed:
  • Gunther Jikeli

    (Indiana University Bloomington)

  • David Axelrod

    (Indiana University Bloomington)

  • Rhonda K. Fischer

    (Indiana University Bloomington)

  • Elham Forouzesh

    (Indiana University Bloomington)

  • Weejeong Jeong

    (Indiana University Bloomington)

  • Daniel Miehling

    (Indiana University Bloomington)

  • Katharina Soemer

    (Indiana University Bloomington)

Abstract

Antisemitism is a global phenomenon on the rise that is negatively affecting Jews and communities more broadly. It has been argued that social media has opened up new opportunities for antisemites to disseminate material and organize. It is, therefore, necessary to get a picture of the scope and nature of antisemitism on social media. However, identifying antisemitic messages in large datasets is not trivial and more work is needed in this area. In this paper, we present and describe an annotated dataset that can be used to train tweet classifiers. We first explain how we created our dataset and approached identifying antisemitic content by experts. We then describe the annotated data, where 11% of conversations about Jews (January 2019–August 2020) and 13% of conversations about Israel (January–August 2020) were labeled antisemitic. Another important finding concerns lexical differences across queries and labels. We find that antisemitic content often relates to conspiracies of Jewish global dominance, the Middle East conflict, and the Holocaust.

Suggested Citation

  • Gunther Jikeli & David Axelrod & Rhonda K. Fischer & Elham Forouzesh & Weejeong Jeong & Daniel Miehling & Katharina Soemer, 2024. "Differences between antisemitic and non-antisemitic English language tweets," Computational and Mathematical Organization Theory, Springer, vol. 30(3), pages 232-266, September.
    • Handle: RePEc:spr:comaot:v:30:y:2024:i:3:d:10.1007_s10588-022-09363-2
    DOI: 10.1007/s10588-022-09363-2
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