Differences between antisemitic and non-antisemitic English language tweets

Gunther Jikeli (), David Axelrod (), Rhonda K. Fischer (), Elham Forouzesh (), Weejeong Jeong (), Daniel Miehling () and Katharina Soemer ()
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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

Computational and Mathematical Organization Theory, 2024, vol. 30, issue 3, No 3, 232-266

Abstract: 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.

Keywords: White supremacy; Hate speech; Antisemitism; Twitter (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10588-022-09363-2

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